Server and method for facilitating green leasing arrangement for property

Abstract

According to various embodiments, there is a server for facilitating a green leasing arrangement for a property to support a green certification system, comprising: a memory for storing instructions; and a processor for executing the stored instructions and configured to: determine one or more candidate tenants to lease one or more leasing spaces of the property based on a consumer behaviour purchasing pattern; map a tenant of the one or more candidate tenants to a leasing space of the one or more leasing spaces based on at least one performance indicator relevant to the tenant; generate a green criterion for the tenant to be included in the green leasing arrangement for the tenant; and update at least one configuration of the green criterion to be included in the green leasing arrangement for the tenant, based on the at least one performance indicator relevant to the tenant.

Description

SERVER AND METHOD FOR FACILITATING GREEN LEASING ARRANGEMENT FOR PROPERTYTECHNICAL FIELD

[0001] Various embodiments relate to a server and a method for facilitating a green leasing arrangement for a property to support a green certification system .BACKGROUND

[0002] The importance on a carbon footprint reduction and a sustainability measure may continue to increase for performance of a property (also referred to as a “building”). For example, a GHG (greenhouse gas) protocol corporate standard may classify a company’s GHG emissions into three scopes. As an example, Scope 1 emissions are direct emissions from owned or controlled sources, Scope 2 emissions are indirect emissions from generation of purchased energy, and Scope 3 emissions are all indirect emissions that occur in a value chain of the company. The Scope 3 emissions may typically account for majority of total corporate emissions. Among them, a leased asset may be one of categories in Scope 3 emissions. The leased asset may be a significant contributor to the company’s Scope 3 emissions if they are used in a way that produces a large amount of greenhouse gas. Carbon emissions related to an operation of leased premises (also referred to as “leased spaces”) where a tenant has an operational control may form a part of carbon footprint of both the tenant and a landlord (also referred to as a “building owner”). Therefore, the landlord may need to tackle with Scope 3 emission for the leased asset, and a green leasing arrangement (also referred to as a “green leasing agreement”) may be required to reach their net zero carbon target. The landlord may need to set a green criterion in the green leasing arrangement so that the tenant may comply with the green criterion during leasing periods.

[0003] In addition, in some countries, all new properties may require to adhere to higher sustainability standards and be green-certified under a green building rating system (for example, green mark standards such as Green Mark 2021 Certification standards). The green building rating system may include green leases as a part of Whole Life Carbon (Cn) Section. The landlord may need to seek a relevant professional advice before including the greencriterion into the green leasing arrangement for the tenant. However, it may be challenging to adopt the green leasing arrangement due to various obstacles, for example, a strict obligation, inflexibility, and a lack of understanding for financial performance.

[0004] Moreover, especially for commercial properties, the landlord may need to achieve a green goal of maintaining the green mark standards aligning with a revenue goal of improving foot traffic and business while also reducing costs. However, the landlord may have a lack of understanding to determine a relevant green criterion for individual tenants to have successful negotiations in the green leasing arrangement without affecting performance levels. In addition, the landlord may have a limited knowledge to curate a good tenant-mix for a leasing space to maintain the green mark standards and to attract the foot traffic.

[0005] Therefore, there exists a need to provide an improved solution to recommend the leasing space and the relevant green criterion, to achieve the green goal of maintaining the green mark certification standards aligning with the revenue goal of improving the foot traffic and business for the property.SUMMARY

[0006] To address the above mentioned problems relating to environmental impacts of a leased property, various embodiments may provide a green lease updating (optimisation) system and method for recommending the leasing space and the relevant green criterion. The various embodiments may identify a tenant-relevant green criterion and leasing space configurations to achieve a healthy mix of well-curated tenants while maintaining green mark certification standards for the leased property.

[0007] According to various embodiments, there is a server for facilitating a green leasing arrangement for a property to support a green certification system, the server comprising: a memory for storing instructions; and a processor for executing the stored instructions and configured to: determine one or more candidate tenants to lease one or more leasing spaces of the property based on a consumer behaviour purchasing pattern; map a tenant of the one or more candidate tenants to a leasing space of the one or more leasing spaces based on at least one performance indicator relevant to the tenant; generate a green criterion for the tenant to be included in the green leasing arrangement for the tenant; and update at least one configuration of the green criterion to be included in the green leasing arrangement for the tenant, based on the at least one performance indicator relevant to the tenant.

[0008] In some embodiments, the processor is configured to: determine the one or more candidate tenants from a plurality of potential tenants based on the consumer behaviour purchasing pattern; and select the tenant from the one or more candidate tenants based on the at least one performance indicator relevant to the tenant.

[0009] In some embodiments, the processor is configured to: extract online consumer behaviour data relating to purchasing and offline consumer behaviour data relating to purchasing; correlate the online consumer behaviour data, the offline consumer behaviour data and a geo-location of the one or more leasing spaces; and predict the consumer behaviour purchasing pattern based on the correlation.

[0010] In some embodiments, the processor is configured to determine the one or more candidate tenants further based on a lease expiry profile of the one or more leasing spaces.

[0011] In some embodiments, the processor is configured to recommend the leasing space for the tenant from the one or more leasing spaces based on the at least one performance indicator relevant to the tenant.

[0012] In some embodiments, the processor is configured to reconfigure the one or more leasing spaces to recommend the leasing space for the tenant based on the at least one performance indicator relevant to the tenant.

[0013] In some embodiments, the processor is configured to generate the green criterion for the tenant to maximise foot traffic for the property.

[0014] In some embodiments, the processor is configured to update the at least one configuration of the green criterion further based on a request from the tenant.

[0015] In some embodiments, the processor is configured to evaluate the green leasing arrangement with the tenant based on at least one of an actual environmental performance and an actual financial performance.

[0016] In some embodiments, the performance indicator includes at least one of an environmental performance, rental fees, and foot traffic relevant to the tenant.

[0017] According to various embodiments, there is a method for facilitating a green leasing arrangement for a property to support a green certification system, the method comprising: determining one or more candidate tenants to lease one or more leasing spaces of the property based on a consumer behaviour purchasing pattern; mapping a tenant of the one or more candidate tenants to a leasing space of the one or more leasing spaces based on at least one performance indicator relevant to the tenant; generating a green criterion for the tenant to be included in the green leasing arrangement for the tenant; and updating at least one configurationof the green criterion to be included in the green leasing arrangement for the tenant, based on the at least one performance indicator relevant to the tenant.

[0018] In some embodiments, the method further comprises: determining the one or more candidate tenants from a plurality of potential tenants based on the consumer behaviour purchasing pattern; and selecting the tenant from the one or more candidate tenants based on the at least one performance indicator relevant to the tenant.

[0019] In some embodiments, the method further comprises: extracting online consumer behaviour data relating to purchasing and offline consumer behaviour data relating to purchasing; correlating the online consumer behaviour data, the offline consumer behaviour data and a geo-location of the one or more leasing spaces; and predicting the consumer behaviour purchasing pattern based on the correlation.

[0020] In some embodiments, the method further comprises determining the one or more candidate tenants further based on a lease expiry profile of the one or more leasing spaces.

[0021] In some embodiments, the method further comprises recommending the leasing space for the tenant from the one or more leasing spaces based on the at least one performance indicator relevant to the tenant.

[0022] In some embodiments, the method further comprises reconfiguring the one or more leasing spaces to recommend the leasing space for the tenant based on the at least one performance indicator relevant to the tenant.

[0023] In some embodiments, the method further comprises generating the green criterion for the tenant to maximise foot traffic for the property.

[0024] In some embodiments, the method further comprises updating the at least one configuration of the green criterion further based on a request from the tenant.

[0025] In some embodiments, the method further comprises evaluating the green leasing arrangement with the tenant based on at least one of an actual environmental performance and an actual financial performance.

[0026] In some embodiments, the performance indicator includes at least one of an environmental performance, rental fees, and foot traffic relevant to the tenant.

[0027] According to various embodiments, a computer program product, comprising instructions to cause the server of any one of the above embodiments to execute the steps of the method of any one of the above embodiments is provided.

[0028] According to various embodiments, a computer-readable medium having stored thereon the above computer program product is provided.

[0029] According to various embodiments, a data processing apparatus configured to perform the method of any one of the above embodiments is provided.

[0030] According to various embodiments, a computer program element comprising program instructions, which, when executed by one or more processors, cause the one or more processors to perform the method of any one of the above embodiments is provided.

[0031] According to various embodiments, a computer-readable medium comprising program instructions, which, when executed by one or more processors, cause the one or more processors to perform the method of any one of the above embodiments is provided. The computer-readable medium may include a non-transitory computer-readable medium.BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In the drawings, like reference characters generally refer to the same parts throughout the different views. The drawings are not necessarily to scale, emphasis instead generally being placed upon illustrating the principles of the invention. In the following description, various embodiments are described with reference to the following drawings, in which:

[0033] FIG. 1 is a block diagram illustrating a server according to various embodiments.

[0034] FIG. 2 is a block diagram illustrating a processor according to various embodiments.

[0035] FIG. 3 is a conceptual diagram illustrating a system according to various embodiments.

[0036] FIG. 4 is a conceptual diagram illustrating a system according to various embodiments.

[0037] FIG. 5 is a data flow diagram illustrating a system according to various embodiments.

[0038] FIG. 6 is a data flow diagram illustrating a foot traffic prediction model according to various embodiments.

[0039] FIG. 7 is a data flow diagram illustrating an environmental performance prediction model according to various embodiments.

[0040] FIG. 8 is a data flow diagram illustrating a rental income prediction model according to various embodiments.

[0041] FIG. 9 is a data flow diagram illustrating an overall tenant ranking model according to various embodiments.

[0042] FIG. 10 is an exemplary diagram illustrating a data structure of suggesting a green criterion combination according to various embodiments.

[0043] FIG. 11 is exemplary diagrams illustrating a green portfolio and a lease expiry profile according to various embodiments.

[0044] FIG. 12 is exemplary diagrams illustrating tenants of a property categorised by a business according to various embodiments.

[0045] FIG. 13 is an exemplary diagram illustrating a system for recommending a tenant to a leasing space according to various embodiments.

[0046] FIG. 14 is an exemplary diagram illustrating a system for recommending a tenant to a leasing space according to various embodiments.

[0047] FIGS. 15A, 15B, 15C and 15D are exemplary diagrams for a user journey illustrating a visual presentation of a system according to various embodiments.

[0048] FIG. 16 is a flow diagram illustrating a method according to various embodiments.

[0049] FIG. 17 is a flow diagram illustrating a method according to various embodiments.DESCRIPTION

[0050] Embodiments described below in context of the method are analogously valid for the server, and vice versa. Furthermore, it will be understood that the embodiments described below may be combined, for example, a part of one embodiment may be combined with a part of another embodiment.

[0051] It will be understood that any property described herein for a specific device may also hold for any device described herein. Furthermore, it will be understood that for any device described herein, not necessarily all the components described must be enclosed in the device, but only some (but not all) components may be enclosed.

[0052] It should be understood that the terms “on”, “over”, “top”, “bottom”, “down”, “side”, “back”, “left”, “right”, “front”, “lateral”, “side”, “up”, “down” etc., when used in the following description are used for convenience and to aid understanding of relative positions or directions, and not intended to limit the orientation of any device, structure or any part of any device or structure. In addition, the singular terms “a”, “an”, and “the” include plural references unless context clearly indicates otherwise. Similarly, the word “or” is intended to include “and” unless the context clearly indicates otherwise.

[0053] The term “coupled” (or “connected”) herein may be understood as electrically coupled or as mechanically coupled, for example attached or fixed, or just in contact without any fixation, and it will be understood that both direct coupling or indirect coupling (in other words: coupling without direct contact) may be provided.

[0054] In order that the invention may be readily understood and put into practical effect, various embodiments will now be described by way of examples and not limitations, and with reference to the figures.

[0055] FIG. 1 is a block diagram illustrating a server 10 according to various embodiments.

[0056] In some embodiments, the server 10, for example, implemented by a server computer, may include a communication interface 100, a processor 200, and a memory 300.

[0057] In some embodiments, the memory 300 (also referred to as a “database”, a “storage” or a “data repository”) may store input data and / or output data temporarily or permanently. In some embodiments, the memory 300 may store program code which allows the server 10 to perform any one of methods S100 and S200 (as will be described with reference to FIGS. 16 and 17). In some embodiments, the program code may be embedded in a Software Development Kit (SDK). The memory 300 may include an internal memory of the server 10 and / or an external memory. The external memory may include, but is not limited to, an external storage medium, for example, a memory card, a flash drive, and a web storage.

[0058] In some embodiments, the communication interface 100 may allow one or more external systems to communicate with the processor 200 via a network. In some embodiments, the communication interface 100 may transmit signals to the external systems, and / or receive signals from the external systems via the network.

[0059] In some embodiments, the communication interface 100 may receive information about online activities relating to purchasing for a plurality of consumers from a first external system via the network. In some embodiments, the communication interface 100 may receive information about offline activities relating to purchasing for the plurality of consumers from a second external system via the network. In some embodiments, the first external system and the second external system are different. In some other embodiments, the first external system and the second external system are the same.

[0060] In some embodiments, the online activities relating to purchasing may refer to the consumers’ activities relating to online purchasing / shopping. The information about the online activities relating to purchasing may include, but is not limited to, the consumers’ visit to website, the consumer’s frequencies of pages visited, and / or the consumers’ clicks. In some embodiments, the information about the online activities relating to purchasing may be obtained by a computer and / or a mobile device using an advertising ID, an IP address, and / or cookies. In some embodiments, the information about the online activities may be collected at a micro-level.

[0061] In some embodiments, the offline activities relating to purchasing may refer to the consumers’ activities relating to offline purchasing / shopping. The information about the offline activities relating to purchasing may include, but is not limited to, consumers’ visit time to a property (also referred to as a “commercial property”, a “building”, a “mall” or a “shopping mall”), consumers’ foot traffic in the property, consumers’ trajectory in the property, the property’s geo-location, offline sale data, and / or offline product data. In some embodiments, the information about the offline activities relating to purchasing may be obtained by one or more sensors installed in the property. For example, the sensor may include a ToF (time-of- flight) sensor. In some embodiments, the information about the offline activities may be collected at a macro-level.

[0062] In some embodiments, the communication interface 100 may allow an external device associated with a landlord of the property (also referred to as a “building owner”, a “property owner” or a “leasing manager”), for example, a device belonging to the landlord, to communicate with the processor 200 via the network. In some embodiments, the communication interface 100 may transmit signals to the external device, and / or receive signals from the external device via the network.

[0063] In some embodiments, the communication interface 100 may allow a plurality of external devices associated with a plurality of tenants, for example, a plurality of devices each belonging to the plurality of tenants, to communicate with the processor 200 via the network. In some embodiments, the communication interface 100 may transmit signals to the external devices, and / or receive signals from the external devices via the network.

[0064] In some embodiments, the communication interface 100 may allow a plurality of external devices associated with the plurality of consumers, for example, a plurality of devices each belonging to the plurality of consumers, to communicate with the processor 200 via the network. In some embodiments, the communication interface 100 may transmit signals to the external devices, and / or receive signals from the external devices via the network.

[0065] In some embodiments, the communication interface 100 may receive a request for facilitating a green leasing arrangement (also referred to as a “green leasing agreement”) from the landlord. Throughout the description, the green leasing arrangement may refer to a leasing arrangement which may be designed to meet requirements of at least one green certification system, for example, LEED (Leadership in Energy and Environmental Design), Energy Star or Green Mark. In some embodiments, the request for facilitating the green leasing arrangement may include at least one of a request for recommending a tenant for a leasing space of theproperty and generating a green criterion to be included in the green leasing arrangement to support the green certification system. The communication interface 100 may then send the request for facilitating the green leasing arrangement to the processor 200.

[0066] In some embodiments, the processor 200 may include, but is not limited to, a microprocessor, an analogue circuit, a digital circuit, a mixed-signal circuit, a logic circuit, an integrated circuit, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), a Digital Signal Processor (DSP), a Field Programmable Gate Array (FPGA), an Application Specific Integrated Circuit (ASIC), or any combination thereof. Any other kind of implementation of the respective functions, which will be described below in further detail, may also be understood as the processor 200.

[0067] In some embodiments, the processor 200 may be connectable to the communication interface 100. In some embodiments, the processor 200 may be arranged in data or signal communication with the communication interface 100 to receive the request for facilitating the green leasing arrangement from the communication interface 100.

[0068] In some embodiments, the processor 200 may receive the information about the online activities relating to purchasing for the plurality of consumers from the communication interface 100. In some embodiments, the processor 200 may receive the information about the offline activities relating to purchasing for the plurality of consumers from the communication interface 100.

[0069] In some embodiments, the processor 200 may analyse the information about the online activities relating to purchasing for the plurality of consumers. In some embodiments, the processor 200 may generate online behaviour data for the plurality of consumers based on the analysed information about the online activities for the plurality of consumers. In some embodiments, the processor 200 may analyse the information about the offline activities relating to purchasing for the plurality of consumers. In some embodiments, the processor 200 may generate offline behaviour data for the plurality of consumers based on the analysed information about the offline activities for the plurality of consumers.

[0070] In some embodiments, the online behaviour data may refer to data, for example, in the form of a collection of discrete values, that may convey the information about the online activities relating to purchasing. In some embodiments, the offline behaviour data may refer to data, for example, in the form of a collection of discrete values, that may convey the information about the offline activities relating to purchasing.

[0071] In some embodiments, the processor 200 may extract the online behaviour data relating to purchasing. In some embodiments, the processor 200 may extract the offline behaviour data relating to purchasing.

[0072] In some embodiments, the processor 200 may correlate the offline behaviour data, the online behaviour data, and a geo-location of one or more leasing spaces of the property, using a spatial-temporal-context parameter. In some embodiments, the processor 200 may then predict a consumer behaviour purchasing pattern for the plurality of consumers based on the correlation. For example, the processor 200 may generate information about the consumer behaviour purchasing pattern for the plurality of consumers based on the correlation.

[0073] In some embodiments, the consumer behaviour purchasing pattern for the plurality of consumers may refer to an online / offline purchasing pattern for the plurality of consumers. For example, the information about the consumer behaviour purchasing pattern is that “consumers tend to go to a cafe located at #01-01 on weekend morning to drink coffee”. As another example, the information about the consumer behaviour purchasing pattern is that “consumers tend to go to a supermarket located at #03-01 on weekday morning to buy groceries”. In this manner, the processor 200 may predict consumer trends. In some embodiments, the consumer trends may include changes in the consumer trends (also referred to as a “changing consumer trends”). In some embodiments, the processor 200 may perform the above-mentioned operations periodically or occasionally.

[0074] In some embodiments, the processor 200 may determine one or more candidate tenants, for example, from a plurality of potential tenants, to lease one or more leasing spaces of the property based on the consumer behaviour purchasing pattern.

[0075] In some embodiments, the processor 200 may obtain information about the one or more leasing spaces that are available for leasing out. For example, the processor 200 may obtain the information about the one or more leasing spaces from an external device associated with the landlord, via the communication interface 100. As another example, the processor 200 may obtain the information about the one or more leasing spaces from the memory 300.

[0076] In some embodiments, the processor 200 may obtain information about the plurality of potential tenants. For example, the processor 200 may obtain the information about the plurality of potential tenants from the external devices each associated with the plurality of potential tenants, via the communication interface 100. As another example, the processor 200 may obtain the information about the plurality of potential tenants from the memory 300. As another example, the processor 200 may obtain the information about the plurality of potential tenantsfrom an external device associated with an agency, for example, a property agency, via the communication interface 100.

[0077] In some embodiments, the processor 200 may filter the plurality of potential tenants, and obtain the one or more candidate tenants that may be suitable for the one or more leasing spaces in the property. For example, the processor 200 may filter the plurality of potential tenants based on the consumer behaviour purchasing pattern. For example, the processor 200 may match the plurality of potential tenants to the predicted consumer trends, for example, the consumer behaviour purchasing pattern. In some embodiments, the processor 200 may suggest a set of tenant lists that may facilitate the changing consumer behaviour purchasing pattern.

[0078] In some embodiments, the processor 200 may map a tenant of the one or more candidate tenants to a leasing space of the one or more leasing spaces based on at least one performance indicator relevant to the tenant. In some embodiments, the processor 200 may select the tenant from the one or more candidate tenants based on the at least one performance indicator relevant to the tenant.

[0079] In some embodiments, the performance indicator may include, but is not limited to, at least one of an environmental performance, rental fees, and foot traffic relevant to the tenant. For example, the performance indicator may be stored in the memory 300. In some embodiments, the environmental performance may refer to whether the tenant has complied with green mark standards and whether the tenant has received an environmental certificate, for example, a green certificate. In some embodiments, the environmental performance may refer to whether the property has complied with the green mark standards and whether the property has received the environmental certificate, for example, the green certificate. In some embodiments, the rental fees may refer to rental income that the landlord would receive from the tenant. In some embodiments, the foot traffic may refer to a predicted number of consumers that enter a store of the tenant. In some embodiments, the processor 200 may determine the one or more candidate tenants further based on a lease expiry profile of the one or more leasing spaces. For example, the lease expiry profile may refer to at least one of a lease expiry date and an available date for new leasing of the one or more leasing spaces.

[0080] In some embodiments, the processor 200 may recommend the leasing space for the tenant from the one or more leasing spaces based on the at least one performance indicator relevant to the tenant. For example, the processor 200 may assign the selected tenant to a leasing space located at #01-01 of the property.

[0081] In some embodiments, the processor 200 may reconfigure the one or more leasing spaces to recommend the leasing space for the tenant based on the at least one performance indicator relevant to the tenant. For example, the processor 200 may combine two leasing spaces located at #01-01 and #01-02 of the property, and assign the selected tenant to the combined leasing spaces located at #01-01 and #01-02. As another example, the processor 200 may divide one leasing space located at #01-01 into two leasing spaces, for example, #01 -01 A and #01-0 IB, and assign a first selected tenant to the divided leasing space located at #01 -01 A, and assign a second selected tenant to the divided leasing space located at #01-0 IB.

[0082] In some embodiments, the processor 200 may generate a green criterion for the tenant to be included in the green leasing arrangement for the tenant. For example, the processor 200 may generate the green criterion to prepare the green leasing arrangement for the tenant, and then the processor 200 may prepare the green leasing arrangement including the generated green criterion. In some embodiments, the processor 200 may generate the green criterion for the tenant to maximise foot traffic for the property. For example, the processor 200 may take the predicted foot traffic for the property into account, when the processor 200 generates the green criterion.

[0083] In some embodiments, the processor 200 may update (also referred to as “revise”, “change” or “optimise”) at least one configuration of the green criterion to be included in the green leasing arrangement for the tenant, based on the at least one performance indicator relevant to the tenant. In some embodiments, the processor 200 may update the at least one configuration of the green criterion further based on a request from the tenant. In some embodiments, the at least one performance indicator used to update the at least one configuration of the green criterion may be the same as the at least one performance indicator used to map the tenant to the leasing space. In some other embodiments, the at least one performance indicator used to update the at least one configuration of the green criterion may be different from the at least one performance indicator used to map the tenant to the leasing space. For example, where the tenant further requests to update the at least one configuration of the green criterion, the at least one performance indicator used to update the at least one configuration of the green criterion may include, but is not limited to, a consideration between green leasing and leasing (rental) costs, adjusting level of the green criterion (to be higher or lower), and a trade-off between the green criterion and the leasing (rental) costs.

[0084] In some embodiments, to update the at least one configuration of the green criterion, the processor 200 may use different levels of data (for example, a tenant level, a property(building) level, and a property owner (building owner) level) based on green portfolio data. In some embodiments, the green criterion for different types of establishments may differ from one another. As an example, a green criterion for a supermarket and a green criterion for a bookshop may differ from each other. For example, the supermarket may consume more energy due to their needs for cooling items. Therefore, the green certification system may relax a certain green criterion for the supermarket. On the other hand, the bookshop may be recommended with stricter energy-efficient measures. In general, the property may maintain energy usage within certain thresholds that account for the varying levels of tenant utilisation. With regards to the property owner level data, for example, where the property owner has three buildings (Buildings A, B and C) in thir portfolio, and Building A adheres to the Green Mark GOLD standards while Buildings B and C strive to achieve Green Mark Platinum Standards, overall, the property owner’s green portfolio remains commendable, with two buildings meeting platinum standards and one meeting gold standards.

[0085] In some embodiments, the processor 200 may evaluate the green leasing arrangement for the tenant, based on at least one of an actual environmental performance and an actual financial performance. In some embodiments, the processor 200 may use the evaluation result to train a machine learning model of the processor 200. Therefore, the processor 200 may provide an improved solution in recommending the tenant to the leasing space and in providing the green criterion.

[0086] As described above, the various embodiments may provide the server and the method for recommending the leasing space and the relevant green criterion. The various embodiments may identify the tenant-relevant green criterion and leasing space configurations to achieve the environmental goal and the revenue goal for the property.

[0087] FIG. 2 is a block diagram illustrating a processor 200 according to various embodiments.

[0088] FIG. 2 illustrates modules of the processor 200 (as described with reference to FIG. 1) for facilitating a green leasing arrangement for a property. As shown in FIG. 2, the processor 200 may include, but is not limited to, a dashboard GUI (graphical user interface) 201 , a change detection module 202, a space making module 203, a green criteria generation module 204, an update module 205 (also referred to as an “optimisation module”), and a carbon footprint calculation module 206. In some embodiments, the space making module 203, the green criteria generation module 204, and the update module 205 may be included in a group.

[0089] In some embodiments, the dashboard GUI 201 may communicate with at least one of the grouped modules 203, 204, 205. In some embodiments, the dashboard GUI 201 may communicate with a user, for example, a landlord of the property. A device associated with the landlord may display a screen relating to the dashboard GUI 201. The landlord may input data on the dashboard GUI 201 , and the input data may be sent to the at least one of the grouped modules 203, 204, 205.

[0090] In some embodiments, the change detection module 202 may detect consumer trends. In some embodiments, the change detection module 202 may receive information about online activities relating to purchasing for a plurality of consumers and information about offline activities relating to purchasing for the plurality of consumers from a communication interface 100 (as described with reference to FIG. 1).

[0091] In some embodiments, the change detection module 202 may analyse the information about the online activities relating to purchasing for the plurality of consumers, and generate online behaviour data for the plurality of consumers based on the analysed information about the online activities for the plurality of consumers. In some embodiments, the change detection module 202 may analyse the information about the offline activities relating to purchasing for the plurality of consumers, and generate offline behaviour data for the plurality of consumers based on the analysed information about the offline activities for the plurality of consumers. In some embodiments, the change detection module 202 may extract the online behaviour data relating to purchasing and the offline behaviour data relating to purchasing.

[0092] In some embodiments, the change detection module 202 may correlate the offline behaviour data, the online behaviour data, and a geo-location of one or more leasing spaces of the property, using a spatial-temporal-context parameter. In some embodiments, the change detection module 202 may then predict a consumer behaviour purchasing pattern based on the correlation.

[0093] In some embodiments, the space making module 203 may recommend a leasing space for the tenant based on at least one performance indicator relevant to the tenant. In some embodiments, the space making module 203 may reconfigure the one or more leasing spaces to recommend the leasing space for the tenant based on the at least one performance indicator relevant to the tenant.

[0094] In some embodiments, the green criteria generation module 204 may generate a green criterion for the tenant to be included in the green leasing arrangement for the tenant. In someembodiments, the green criteria generation module 204 may generate the green criterion for the tenant to maximise foot traffic for the property.

[0095] In some embodiments, the update module 205 may update at least one configuration of the green criterion to be included in the green leasing arrangement for the tenant, based on the at least one performance indicator relevant to the tenant. In some embodiments, the update module 205 may update the at least one configuration of the green criterion further based on a request from the tenant.

[0096] In some embodiments, the operations of the space making module 203, the green criteria generation module 204, and the update module 205 may be repeated. For example, after the update module 205 updates the at least one configuration of the green criterion, the space making module 203 may re-recommend a leasing space for the tenant based on the at least one performance indicator relevant to the tenant. As an example, the space making module 203 may re -recommend the leasing space for the tenant further based on the updated configuration of the green criterion, when the space making module 203 may change the space for the tenant. In some embodiments, the agent may receive the state (also referred to as a “state input”) from the environment. For example, the state input received from the environment may include, but is not limited to, promotions, shop location changes, size changes and other marketing factors. In some embodiments, the agent may receive a reward input from the environment. For example, the reward input may include, but is not limited to, scored performance of the agent based on the actions it takes in the environment, to be realistic simulation. In some embodiments, the environment may receive an output from the agent. For example, the output may be an output of the reinforcement learning algorithm. As an example, the output may include, but is not limited to, an action selected by the agent based on its policy and the current state.

[0097] In some embodiments, a data repository 300 may store tenant profiles for a plurality of potential tenants. The tenant profiles may include, but are not limited to, at least one of a business category and a brand reputation of each tenant. In some embodiments, the data repository 300 may store mall statistic data. The mall statistic data may include, but is not limited to, revenue (for example, including rental income), foot traffic (for example, time-series foot traffic), a lease expiry profile, and a tenant mix layout. In some embodiments, the data repository 300 may store building performance data. The building performance data may include, but is not limited to, a utilities usage pattern and a carbon emission report. In some embodiments, the data repository 300 may store green portfolio data. The green portfolio datamay include, but is not limited to, green buildings data, green leases data, and green certifications data relevant to one or more properties owned by the landlord.

[0098] FIG. 5 is a data flow diagram illustrating a system according to various embodiments.

[0099] In some embodiments, a processor 200 (as described with reference to FIG. 1, and Sla and Sib of FIG. 3) may predict changing consumer behaviour purchasing patterns after online / offline micro-macro level correlation based on a geo-location of a property (mall). The processor 200 may suggest a set of tenant lists (one or more candidate tenants) that may facilitate the changing consumer behaviour purchasing pattern.

[0100] As shown in FIG. 5, in some embodiments, offline data may include sensor data which may be extracted from sensors installed inside the mall, and POS transaction data which may be extracted from multiple tenants (retail stores) inside the mall. In some embodiments, omnichannel transaction data may be collected using the POS transaction data and online transaction data. In some embodiments, location data of the retail stores and the malls may be collected.

[0101] In some embodiments, social trends data may be collected.

[0102] In some embodiments, online data may be captured from the e-commerce store (also referred to as an “e-store”) or website, using the mobile ID and / or the IP address.

[0103] In some embodiments, consumers’ lifestyle data, for example, the consumer’s residence location, income, family, timestamp, visiting frequency to the mall, and / or interests at specific time interval, may be collected.

[0104] In some embodiments, an offline data extractor 207 of the processor 200 may model the information about the offline activities, for example, the timestamp, number of consumers at a point of interest, consumer from-to movement (retail stores to retail stores), dwelling time at visited retail stores, average spending per consumers, frequency per consumers, and product category purchased by consumers.

[0105] In some embodiments, an online data extractor 208 of the processor 200 may model the information about the online activities, for example, timestamp, shopping frequency at online shopping mall, visited shopping category / merchants, and similar and / or related product category viewed, purchased, searched, reviewed or dropped by the consumer.

[0106] In some embodiments, a deep learning model may be used to discover related features from the online behaviour data and the offline behaviour data.

[0107] In some embodiments, the modelled information about the offline activities and the modelled information about the online activities may be synchronised.

[0108] In some embodiments, the consumer profile / interest prediction model 209 may include the consumer profiles with associated product categories, candidates retail stores, and / or preferred time periods (date / time / week) along with changed purchasing pattern to be prioritized. In some embodiments, consumer characteristics and preference may be predicted based on the spatial-temporal-context parameter, and similar and / or related product relationship.

[0109] In some embodiments, a set of tenant lists that may facilitate changing consumer behaviour purchasing pattern may be obtained.

[0110] FIG. 6 is a data flow diagram illustrating a foot traffic prediction model 210 according to various embodiments.

[0111] In some embodiments, a processor 200 (as described with reference to FIG. 1, and S2 of FIG. 3) may receive a set of tenant lists (one or more candidate tenants) and information about one or more leasing spaces of a property (211). For example, the information about one or more leasing spaces may include a location and a size of the leasing spaces.

[0112] In some embodiments, the processor 200 may include the foot traffic prediction model 210. In some embodiments, the foot traffic prediction model 210 may load a current tenant-mix layout (213). In some embodiments, the foot traffic prediction model 210 may extract a temporal foot traffic pattern based on the layout of the property (214). In some embodiments, the foot traffic prediction model 210 may extract foot traffic density based on a product category of the tenant (215). In some embodiments, the foot traffic prediction model 210 may generate positional influence factors based on the foot traffic density of the product category and the layout of the property (216). In some embodiments, the foot traffic prediction model 210 may generate external influence factors based on a store reputation and / or promotion data of the tenant (217).

[0113] In some embodiments, the processor 200 may simulate temporal foot traffic for a new tenant based on a business category, a placement of the positional influence factors, and a placement of the external influence factors (212).

[0114] In some embodiments, the processor 200 may map the tenant of the one or more candidate tenants to a leasing space of the one or more leasing spaces. For example, the processor 200 may output a mapping table including a predicted foot traffic scale (218).

[0115] FIG. 7 is a data flow diagram illustrating an environmental performance prediction model 220 according to various embodiments.

[0116] In some embodiments, a processor 200 (as described with reference to FIG. 1, and53 of FIG. 3) may receive a set of tenant lists (one or more candidate tenants) and information about one or more leasing spaces of a property (221). For example, the information about one or more leasing spaces may include a location and a size of the leasing spaces.

[0117] In some embodiments, the processor 200 may include the environmental performance prediction model 220. In some embodiments, the environmental performance prediction model 220 may extract a green criterion based on a tenant profile and / or similar tenant profiles (also referred to as a “green criterion of historical green leases”) (223). In some embodiments, the environmental performance prediction model 220 may extract actual environmental data from the property (224). For example, the actual environmental data may include, but is not limited to, at least one of energy usage, water usage, waste management, air quality, fit-out material, and facility management data. In some embodiments, the environmental performance prediction model 220 may determine a usage pattern based on a product category of the tenant, traffic density and a layout of the property (225).

[0118] In some embodiments, the processor 200 may simulate an environmental performance for a new tenant based on a combination of the green criterion of the historical green leases, respective environmental performance data, and the usage pattern (222).

[0119] In some embodiments, the processor 200 may determine the environmental performance to generate the green criterion for the tenant. For example, the processor 200 may output a table including a predicted environmental performance scale (226).

[0120] FIG. 8 is a data flow diagram illustrating a rental income prediction model 230 according to various embodiments.

[0121] In some embodiments, a processor 200 (as described with reference to FIG. 1, and54 of FIG. 3) may receive a set of tenant lists (one or more candidate tenants) and information about one or more leasing spaces of a property (231). For example, the information about one or more leasing spaces may include a location and a size of the leasing spaces.

[0122] In some embodiments, the processor 200 may include the rental income prediction model 230. In some embodiments, the rental income prediction model 230 may update a tenant layout based on foot traffic and an environmental performance of the tenant (233). In some embodiments, the rental income prediction model 230 may determine a baseline, based on the leased space and a type of the tenant (234). In some embodiments, the rental income prediction model 230 may extract rental income per square feet for a specific tenant type, a minimumamount of leasing space allocated to the specific tenant type, and a maximum amount of the leasing space allocated to the specific tenant type (235).

[0123] In some embodiments, the processor 200 may predict rental income and energy saving after a potential tenant moves in (232). For example, the processor 200 may output a table including a predicted rental income scale (236).

[0124] FIG. 9 is a data flow diagram illustrating an overall tenant ranking model according to various embodiments.

[0125] In some embodiments, a processor 200 (as described with reference to FIG. 1, and S5 of FIG. 3) may obtain predicted variables including, for example, foot traffic density and environmental performance data, per a predetermined size, for example, per square meter, for a predetermined time for each leased space (241).

[0126] In some embodiments, the processor 200 may benchmark with actual foot traffic (inflow) for the leased space (242).

[0127] In some embodiments, the processor 200 may check if a deviation is acceptable or higher than a threshold to a landlord of a property (243). In some embodiments, the landlord of the property may input the threshold (for example, a numerical parameter) in terms of an acceptable range. For example, the threshold may relate to average traffic to the leased space or average utility usage for the leased space. In some embodiments, if the deviation is acceptable or higher than the threshold, the processor 200 may benchmark with actual environmental performance data for the leased property (244). In some embodiments, if the deviation is not acceptable or higher than the threshold, the processor 200 may assign low ranking for tenant attractiveness (246).

[0128] In some embodiments, after the processor 200 benchmarks with actual environmental performance data for the leased property (244), the processor 200 may check if a deviation is acceptable or higher than a predetermined KPI to the landlord of the property (245). In some embodiments, if the deviation is acceptable or higher than the predetermined KPI to the landlord of the property, the processor 200 may assign high ranking for tenant attractiveness (248). In some embodiments, if the deviation is not acceptable or higher than the predetermined KPI to the landlord of the property, the processor 200 may assign medium ranking for tenant attractiveness (247).

[0129] In some embodiments, the processor 200 may re-generate overall tenant ranking, hourly foot traffic per square meter, and total ratio (%) of the whole building (property) contribution (249).

[0130] In some embodiments, the processor 200 may re-aggregate the overall tenant ranking, daily foot traffic per square meter, daily environmental performance data, and the total ratio (%) of the whole property contribution (250).

[0131] In some embodiments, the processor 200 may update a green leasing arrangement, for example, the green criterion, for example, as shown in the table including the rank (251).

[0132] FIG. 10 is an exemplary diagram illustrating a data structure of suggesting a green criterion combination according to various embodiments.

[0133] In some embodiments, a processor 200 (as described with reference to FIG. 1) may obtain information about a business category of a tenant, predicted revenue (tenant level and / or property level), predicted foot traffic (tenant level and / or property level), a predicted environmental performance (tenant level and / or property level), and a target green mark certification (for example, ratio (%) of whole property contribution). In some embodiments, the processor 200 may provide overall ranking of the tenant based on the obtained information.

[0134] In some embodiments, the processor 200 may suggest leasing space configurations, for example, including a location, a position, a size, a GFA (gross floor area), and fixtures.

[0135] In some embodiments, the processor 200 may suggest the green criterion combination. For example, if there are nine candidate green criteria (“A” to “I”), the processor 200 may select green criteria of “A”, “C”, “D”, “E”, “G” and “H” for the tenant, based on the obtained information.

[0136] FIG. 11 is exemplary diagrams illustrating a green portfolio and a lease expiry profile according to various embodiments. As an example, a landlord may own a plurality of properties (buildings).

[0137] In some embodiments, a processor 200 (as described with reference to FIG. 1) may provide the green portfolio for the plurality of properties owned by the landlord. For example, the green portfolio may include, but is not limited to, information about a property identifier (ID), a property name, an award that the property received, awarded year, a target award, renewal year, and ratio (%) of green leases (ratio (%) of green leasing arrangements including a green criterion).

[0138] In some embodiments, the processor 200 may provide the lease expiry profile for the plurality of properties owned by the landlord. For example, the lease expiry profile may include, but is not limited to, information about a property ID, a property name, a number of expiring leases in a certain year, a size of leased spaces (leased area) of expiring leases in the certain year, and ratio (%) of leased spaces of the property in the certain year.

[0139] FIG. 12 is exemplary diagrams illustrating a tenant mix profile according to various embodiments.

[0140] In some embodiments, a processor 200 (as described with reference to FIG. 1) may provide a tenant mix profile. For example, the tenant mix profile may include, but is not limited to, information about ratio (%) of leased spaces (leased area) of each business category of a plurality of tenants. For example, the tenant mix profile may include, but is not limited to, information about ratio (%) of leased spaces (leased area) of each tenant.

[0141] FIG. 13 is an exemplary diagram illustrating a system for recommending a tenant to a leasing space according to various embodiments.

[0142] In some embodiments, a processor 200 (as described with reference to FIG. 1) may provide a plurality of suggestions of configurations of leased spaces. For example, the processor 200 may suggest to assign a first tenant, a second tenant and a third tenant to #02- 03, #02-04 and #02-05 of a property, respectively (hereinafter, referred to as a “first configuration”). As another example, the processor 200 may combine two leasing spaces located at #02-03 and #02-04 of the property, and assign the first tenant to the combined leasing spaces #02-03 and #02-04 of the property, and assign the second tenant to #02-05 of the property (hereinafter, referred to as a “second configuration”).

[0143] FIG. 14 is an exemplary diagram illustrating a system for recommending a tenant to a leasing space according to various embodiments.

[0144] In some embodiments, In some embodiments, a processor 200 (as described with reference to FIG. 1) may rank a plurality of suggestions of configurations of leased spaces (as described with reference to FIG. 13). In some embodiments, the processor 200 may obtain information about simulated foot traffic, simulated energy usage, simulated revenue, ratio (%) of total building (property), daily foot traffic scale, daily environmental performance scale, and ratio (%) of total buildings’ daily foot traffic. In some embodiments, the processor 200 may score each of the plurality of configurations based on the obtained information, and rank the plurality of configurations according to the score. For example, the first and second configurations may be ranked as “high”, and the other configurations may be ranked as “low”.

[0145] FIGS. 15A to 15D are exemplary diagrams for a user journey illustrating a visual presentation of a system 1 according to various embodiments.

[0146] As shown in FIG. 15 A, a user (also referred to as a “leasing manager”) may access the system 1. For example, the user may wish to focus on green certification and find ways to balance revenue projection with sustainability goals, in the circumstances where 30% of mallspace expires this year. The system 1 may load a building layout (step 1). In the step 1, the user may provide the building floor layout, and it may involve using a digital mapping tool to create a detailed and accurate representation of the building layout. The user may further provide a tenant layout (for example, spatial presentation for the tenant). The system 1 may convert the user’s input into a digital map of a building environment.

[0147] Thereafter, the system 1 may show a screen (as shown in FIG. 15B) for the user to fill in a green leasing parameter (step 2). In the step 2, the user may provide a list of at least one of potential tenants, lease expiry profiles, a green KPI, and space configuration for the building. It may involve inputting information about each tenant, including, but not limited to, their business type, size, and location preferences. The green KPI may include targeted green certification, etc. The space configuration may include mapping out location of new stores. Different scenarios may be created in the step 2.

[0148] Thereafter, the system 1 may show a screen (as shown in FIG. 15C) for the user to input consumer’s (shopper’s) information, for example, consumer’s characteristics and consumer’s demographics (step 3). The step 3 may allow the user to simulate different scenarios and identify how different demographics and behaviours may affect traffic flow.

[0149] Thereafter, the system 1 may analyse and update (optimise) the green leasing arrangement (step 4) and show a screen relating to a green leasing update (optimisation) (as shown in FIG. 15D). In the step 4, the user may set thresholds for key KPIs for benchmarking to be in the acceptable range for the different scenarios. The key KPIs may be for a revenue projection, a green goal projection, etc.

[0150] Thereafter, a negotiation with the tenant may be proceeded (step 5). For example, relevant space and criteria combination for green leases may be updated. If the tenant requests changes on the lease term, the space allocation and the green criterion for the re-update (reoptimisation), the step 2 may proceeded again.

[0151] FIG. 16 is a flow diagram illustrating a method SI 00 according to various embodiments.

[0152] In some embodiments, the method SI 00 may include a step SI 10 of determining one or more candidate tenants to lease one or more leasing spaces of the property based on a consumer behaviour purchasing pattern.

[0153] In some embodiments, the method S100 may include a step S120 of mapping a tenant of the one or more candidate tenants to a leasing space of the one or more leasing spaces based on at least one performance indicator relevant to the tenant.

[0154] In some embodiments, the method S100 may include a step S130 of generating a green criteria for the tenant to be included in the green leasing arrangement for the tenant.

[0155] In some embodiments, the method SI 00 may include a step SI 40 of updating at least one configuration of the green criteria to be included in the green leasing arrangement for the tenant, based on the at least one performance indicator relevant to the tenant.

[0156] FIG. 17 is a flow diagram illustrating a method S200 according to various embodiments.

[0157] In some embodiments, the method S200 may include a step S210 of receiving a set of tenant lists which may facilitate changing consumer behaviour purchasing pattern based on spatial, temporal, and similar / related product. In some embodiments, the step S210 may perform operations described with reference to SI of FIG. 3.

[0158] In some embodiments, the method S200 may include a step S220 of mapping suggested tenants to leasing spaces by predicting foot traffic density based on tenant’s needs, a business category, a brand reputation and a placement of positional influence factors, and a placement of external influence factors. In some embodiments, the step S220 may perform operations described with reference to S2 of FIG. 3.

[0159] In some embodiments, the method S200 may include a step S230 of generating a relevant green criterion for each tenant by analysing historical data on environmental performance and usage patterns which may be correlated with various combinations of green criteria from green leases. In some embodiments, the step S230 may perform operations described with reference to S3 of FIG. 3.

[0160] In some embodiments, the method S200 may include a step S240 of computing revenue variations over a duration of leased period based on various combination of space reconfigurations and various combinations of green criteria inclusion by a tenant level, a business category level, and a building level.

[0161] In some embodiments, the method S200 may include a step S250 of determining one of green leasing arrangements based on comparison of benchmarks of financial performance and environmental performance using one or more KPIs. In some embodiments, the steps S240 and S250 may perform operations described with reference to S4 of FIG. 3.

[0162] In some embodiments, the method S200 may include a step S260 of checking if a threshold is acceptable. In some embodiments, the threshold may refer to a numerical parameter input by the landlord. In some embodiments, the update (optimisation) of the green leasing arrangements may be based on the numerical parameter defined by the landlord.

[0163] In some embodiments, the method S200 may include a step S270 of, if the threshold is not acceptable, checking if there is a request for change from the tenant.

[0164] In some embodiments, the method S200 may include a step S280 of, if the threshold is acceptable or if there is the request for change from the tenant, determining relevant requirements based on the tenant’s needs and / or the landlord’s needs, for example, a leasing space and layout, and the green criterion. In some embodiments, the steps S260, S270 and S280 may perform operations described with reference to S5 and S6 of FIG. 3.

[0165] In some embodiments, the method S200 may repeat the steps S220 and S230, after determining the relevant requirements based on the tenant’s needs and / or the landlord’s needs.

[0166] While embodiments of the invention have been particularly shown and described with reference to specific embodiments, it should be understood by those skilled in the art that various changes in form and detail may be made therein without departing from the spirit and scope of the invention as defined by the appended claims. The scope of the invention is thus indicated by the appended claims and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced. It will be appreciated that common numerals, used in the relevant drawings, refer to components that serve a similar or the same purpose. As used herein, the singular forms “a”, “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises” and / or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof. Unless specifically stated otherwise, the term “some” refers to one or more. Combinations such as “at least one of A, B, or C,” “one or more of A, B, or C,” “at least one of A, B, and C,” “one or more of A, B, and C,” and “A, B, C, or any combination thereof’ include any combination of A, B, and / or C, and may include multiples of A, multiples of B, or multiples of C. Specifically, combinations such as “at least one of A, B, or C,” “one or more of A, B, or C,” “at least one of A, B, and C,” “one or more of A, B, and C,” and “A, B, C, or any combination thereof’ may be A only, B only, C only, A and B, A and C, B and C, or A and B and C, where any such combinations may contain one or more member or members of A, B, or C. All structural and functional equivalents to the elements of the various aspects described throughout this disclosure that are known or later come to be known to those of ordinary skill in the art are expressly incorporated herein by reference and are intended to be encompassed by the claims. Moreover, nothingdisclosed herein is intended to be dedicated to the public regardless of whether such disclosure is explicitly recited in the claims. The words “module,” “mechanism,” “element,” “device,” and the like may not be a substitute for the word “means.” As such, no claim element is to be construed as a means plus function unless the element is expressly recited using the phrase “means for.”

Claims

AMENDED CLAIMS received by the International Bureau on 01 October 2024 (01.10.2024)1. A server for facilitating a green leasing arrangement for a property to support a green certification system, the server comprising: a memory for storing instructions; and a processor for executing the stored instructions and configured to: determine a plurality of candidate tenants to lease leasing spaces of the property based on a consumer behaviour purchasing pattern and / or business categories of the plurality of candidate tenants; map each of the plurality of candidate tenants to a leasing space of the leasing spaces based on at least one predicted performance indicator relevant to the each of the plurality of candidate tenants; generate a green criterion for the each of the plurality of candidate tenants to be included in the green leasing arrangement for the each of the plurality of candidate tenants; update at least one configuration of the green criterion to be included in the green leasing arrangement for the each of the plurality of candidate tenants, based on the at least one predicted performance indicator relevant to the each of the plurality of candidate tenants; and report a combination of the green criterion of the plurality of candidate tenants in the green leasing arrangement.

2. The server according to claim 1, wherein the processor is configured to: determine the plurality of candidate tenants from a plurality of potential tenants based on the consumer behaviour purchasing pattern and / or the business categories of the plurality of candidate tenants.

3. The server according to claim 1 or claim 2, wherein the processor is configured to: extract online consumer behaviour data relating to purchasing and offline consumer behaviour data relating to purchasing; correlate the online consumer behaviour data, the offline consumer behaviour data and a geo -location of the leasing spaces; and predict the consumer behaviour purchasing pattern based on the correlation.30AMENDED SHEET (ARTICLE 19)4. The server according to any one of claims 1 to 3, wherein the processor is configured to determine the plurality of candidate tenants further based on a lease expiry profile of the leasing spaces.

5. The server according to any one of claims 1 to 4, wherein the processor is configured to recommend the leasing space for the each of the plurality of candidate tenants from the leasing spaces based on the at least one predicted performance indicator relevant to the each of the plurality of candidate tenants.

6. The server according to claim 5, wherein the processor is configured to reconfigure the leasing spaces to recommend the leasing space for the each of the plurality of candidate tenants based on the at least one predicted performance indicator relevant to the each of the plurality of candidate tenants.

7. The server according to any one of claims 1 to 6, wherein the processor is configured to generate the green criterion for the each of the plurality of candidate tenants to maximise foot traffic for the property.

8. The server according to any one of claims 1 to 7, wherein the processor is configured to update the at least one configuration of the green criterion further based on a request from the each of the plurality of candidate tenants.

9. The server according to any one of claims 1 to 8, wherein the processor is configured to evaluate the green leasing arrangement with the each of the plurality of candidate tenants based on at least one of an actual environmental performance and an actual financial performance.

10. The server according to any one of claims 1 to 6, wherein the predicted performance indicator includes at least one of an environmental performance, rental fees, and foot traffic relevant to the each of the plurality of candidate tenants.

11. A method for facilitating a green leasing arrangement for a property to support a green certification system, the method comprising:31AMENDED SHEET (ARTICLE 19)determining a plurality of candidate tenants to lease leasing spaces of the property based on a consumer behaviour purchasing pattern and / or business categories of the plurality of candidate tenants; mapping each of the plurality of candidate tenants to a leasing space of the leasing spaces based on at least one predicted performance indicator relevant to the each of the plurality of candidate tenants; generating a green criterion for the each of the plurality of candidate tenants to be included in the green leasing arrangement for the each of the plurality of candidate tenants; updating at least one configuration of the green criterion to be included in the green leasing arrangement for the each of the plurality of candidate tenants, based on the at least one predicted performance indicator relevant to the each of the plurality of candidate tenants; and report a combination of the green criterion of the plurality of candidate tenants in the green leasing arrangement.

12. The method according to claim 11 further comprising: determining the plurality of candidate tenants from a plurality of potential tenants based on the consumer behaviour purchasing pattern and / or the business categories of the plurality of candidate tenants.

13. The method according to claim 11 or claim 12 further comprising: extracting online consumer behaviour data relating to purchasing and offline consumer behaviour data relating to purchasing; correlating the online consumer behaviour data, the offline consumer behaviour data and a geo-location of the leasing spaces; and predicting the consumer behaviour purchasing pattern based on the correlation.

14. The method according to any one of claims 1 1 to 13 further comprising determining the plurality of candidate tenants further based on a lease expiry profile of the leasing spaces.

15. The method according to any one of claims 11 to 14 further comprising recommending the leasing space for the each of the plurality of candidate tenants from the leasing spaces based on the at least one predicted performance indicator relevant to the each of the plurality of candidate tenants.32AMENDED SHEET (ARTICLE 19)16. The method according to claim 15 further comprising reconfiguring the leasing spaces to recommend the leasing space for the each of the plurality of candidate tenants based on the at least one predicted performance indicator relevant to the each of the plurality of candidate tenants.

17. The method according to any one of claims 11 to 16 further comprising generating the green criterion for the each of the plurality of candidate tenants to maximise foot traffic for the property.

18. The method according to any one of claims 11 to 17 further comprising updating the at least one configuration of the green criterion further based on a request from the each of the plurality of candidate tenants.

19. The method according to any one of claims 11 to 18 further comprising evaluating the green leasing arrangement with the each of the plurality of candidate tenants based on at least one of an actual environmental performance and an actual financial performance.

20. The method according to any one of claims 11 to 16, wherein the predicted performance indicator includes at least one of an environmental performance, rental fees, and foot traffic relevant to the each of the plurality of candidate tenants.33AMENDED SHEET (ARTICLE 19)

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