Estimating market value of farmland

Abstract

A computer-implemented method for estimating the fair market value of farmland, the method comprising: providing a computer server having a processor and memory, and at least one client device with a user interface for displaying information, wherein the computer server comprises a search input, an output generator, a geographical database, a machine learning model, an image processing module, and a sales database. The computer server is connected to the client device over a network. The client device receives a query relating to the farmland and transmits the query to the search input in the memory. Land information relating to the query is accessed from the geographical database. The method identifies, through the geographical database, at least one comparable farmland. The farmland is analyzed through the machine learning model and the image processing module on thousands of data points related to the farmland. The machine learning models generate a fair market value output for the farmland and transmit the fair market value of the farmland to the client device using the output generator.

Description

ESTIMATING MARKET VALUE OF FARMLAND BACKGROUND

[0001] The present invention pertains to the field of agricultural real estate appraisal and market analytics. More specifically, it relates to a computer-implemented system and method for estimating the fair market value of farmland. The system utilizes machine learning models, image processing techniques, weather data, and a geographical database to analyze various data points related to farmland and generate an estimate of its fair market value. SUMMARY

[0002] The following provides a simplified summary of various aspects of this disclosure to offer a basic understanding. This summary is not an exhaustive overview of all contemplated aspects and is neither intended to identify key or critical elements nor delineate the scope of such aspects. Its purpose is to introduce some concepts in a simplified form as a prelude to the more detailed description provided later.

[0003] In various implementations, this disclosure describes a computer-implemented method for estimating the fair market value of farmland. The method involves using a computer server equipped with a processor and memory, supporting a client device interface. This setup facilitates the display and interaction of information. The key components of the server include a search input for receiving queries, an output generator, a geographical database for storing land data, a machine learning model to analyze data points, an image processing module to process farmland images, and machine learning models to calculate the farmland's value. The process begins when the client device submits a query about specific farmland to the server. The server retrieves relevant information from the geographical database and identifies comparable properties. It then analyzes these properties using the machine learning and image processing modules. Finally, the estimated farmland value is calculated and sent back to the client device through the output generator.

[0004] The following paragraphs provide additional support for the claims of this application.

[0005] Farmland sales data is collected from various sources and entered into the database. Each sale record includes land size, shape, price, price per acre, and assessment details. Additionally, arability and soil information are calculated at the time of the sale. This information is used in real time to evaluate a given parcel of farmland and identify2 comparable sales from historical data based on these features. Parameters such as the distance between the given farmland and other sales, as well as current market conditions, are also considered when adjusting the estimated value.

[0006] These and other features and advantages will become apparent upon reading the following detailed description and reviewing the appended drawings. It is to be understood that the foregoing summary, the following detailed description, and the appended drawings are explanatory only and do not limit or restrict the scope of the claimed aspects.

[0007] In one embodiment, the microprocessor identifies a pre-set number of comparable properties and calculates a data confidence score for the estimated farmland value.

[0008] In another embodiment, the remote server allows the user to export a selected number of comparable properties and their associated data, including farmland information, neighborhood details, satellite images, soil maps, and polygons of the farmland. Additionally, the remote server generates a graphical representation comprising a comprehensive land history and a land improvement value for each comparable farmland.

[0009] In another embodiment, the remote server enables users to generate custom reports based on their own comparables or those suggested by the system. The available information is auto-suggested by the remote server for convenience.

[0010] In another embodiment, comparable properties are sorted based on at least one sorting key, such as nominal distance, arable acres, price, or soil capability.3 BRIEF DESCRIPTION OF THE DRAWINGS

[0011] FIG. 1 is a block diagram of a farm appraisal system in accordance with the subject disclosure.

[0012] FIG. 2 is an exemplary process in accordance with the subject disclosure.

[0013] FIGS. 3A-3H illustrate an exemplary user interface implemented in the farmland appraisal system.

[0014] FIG. 4 is an exemplary cloud computing system in accordance with the subject disclosure.

[0015] FIG. 5 is an exemplary computer system in accordance with the subject disclosure. DETAILED DESCRIPTION

[0016] The subject disclosure relates to a cloud-based platform for estimating the fair market value of farmland, and more specifically, to a cloud-based farm appraisal system that enables computer-implemented, automated farmland appraisal. The system can store various information about properties, which can be retrieved from the cloud server by a user. This information can also be exported as a portable document file (PDF) in response to user input.

[0017] The detailed description provided below, in connection with the appended drawings, is intended to serve as an example and is not meant to represent the only possible forms in which the present examples can be constructed or utilized. The description outlines the functions of the examples and sequences of steps for constructing and operating the examples. However, equivalent functions and sequences can also be achieved through different implementations.

[0018] References to terms such as “one embodiment,” “an embodiment,” “an example,” “one implementation,” and similar phrases indicate that the described embodiment, implementation, or example may include a particular feature, structure, or characteristic. However, not every embodiment, implementation, or example will necessarily include that specific feature, structure, or characteristic. Additionally, these terms do not always refer to the same embodiment, implementation, or example. When a specific feature, structure, or characteristic is described in connection with an embodiment, implementation, or example, it is understood that such a feature, structure, or characteristic may also be implemented in other embodiments, implementations, or examples, even if not explicitly described.4

[0019] References to a “module,” “software module,” and similar terms indicate a software component or a part of a program, application, or app that contains one or more routines. One or more independent modules may together comprise a program, application, or app.

[0020] References to an “app,” “application,” or “software application” refer to a computer program or group of programs designed for end users. These terms encompass standalone applications, thin-client applications, thick-client applications, web-based applications (such as browser-based apps), and other similar types of applications.

[0021] Numerous specific details are provided to ensure a thorough understanding of one or more embodiments of the described subject matter. However, it is noted that these embodiments may also be practiced without adhering to all of the specific details.

[0022] A cloud-based automated valuation platform for farmland is provided. The platform includes at least one database for farmlands in a given geographical region. This database is used to store data such as soil type, arable acreage, known market value, and other relevant records. The platform features user interfaces accessible via various personal computing devices, including mobile phones, laptops, desktop computers, and tablets.

[0023] The cloud-based platform includes components designed to estimate the fair market value of relevant farmlands. Through any of the user interfaces, a user can search for farms or farmland by entering an address (which may vary based on the province where the user is searching) into the platform. The platform’s database is configured to provide a collection of data points associated with the farmland corresponding to the entered address.

[0024] The platform utilizes computer-implemented algorithms and satellite images to determine the soil type and arable acreage of the farmland linked to the address. These characteristics are extracted or calculated from the extensive data points and satellite imagery associated with the specified farmland.

[0025] The platform is equipped to use advanced machine learning models and image processing technology to analyze thousands of data points, enabling it to estimate the fair market value of the farmland associated with the address. These machine learning models and image processing technologies are implemented to operate through the cloud computing network connecting the database to the user interfaces.

[0026] The platform is designed to display a high and low fair market value range for farmlands in a specified geographical location. Users can also query the platform to5 display comparable farmlands that meet certain search criteria. These comparable farmlands can be retrieved from the user’s farmland database or other accessible databases within the cloud computing network. The platform can organize comparable farmlands by metrics such as soil type, arable acreage, or other parameters. Adjustment factors can also be applied to the comparable farmland data to assist in determining fair market values.

[0027] Various features of the subject disclosure are described in more detail below with reference to the drawings, where similar numerals generally refer to similar or corresponding elements throughout. The drawings and descriptions are not intended to limit the claimed subject matter to the specific forms described. Instead, the intention is to cover all modifications, equivalents, and alternatives within the spirit and scope of the claimed subject matter.

[0028] Now referring to the drawings and particularly to FIG. 1, various features of the subject disclosure are described in more detail with respect to a cloud-based farmland appraisal system, generally designated with the numeral 100. The system 100 includes a cloud server 110 connected to a computing device 112 over a network 114. The cloud server 110 includes a processor 116 and memory 118.

[0029] The computing device 112 can feature a user interface, such as a web browser 120, for displaying various farmland-related information, including fair market value, geographic data, soil type, and arable acreage. The user interface can also be part of a mobile app. The information displayed is generated by the cloud server 110 based on input received on the computing device 112 and transmitted to the cloud server 110 over the network 114.

[0030] The cloud server 110 hosts a platform 122 that includes a search input 124, an output generator 126, a geographical database 128, a machine learning model 130, and an image processing module 134.

[0031] The search input 124 enables users to search for farmlands by entering an address into the platform. Once one or more properties are identified, the platform 122 displays a high and low fair market value range on the browser 120 of the computing device 112.

[0032] The platform 122 can also display various information, such as geographical data, soil type, and arable acreage, on the browser 120. In an exemplary embodiment, the information may include up to ten comparable farmlands. The comparable farmlands can be categorized by soil type or arable acreage.6

[0033] The output generator 126 can produce outputs that display comprehensive farmland information on the browser 120.

[0034] The geographical database 128 stores and provides all farmland information, including map data, satellite images, and street-view photos, which can be compiled into a custom portable document (PDF) report for the user. The geographical database 128 can also be queried by users to identify or compare relevant farmlands.

[0035] The platform 122 implements machine learning models 130 and the image processing module 134 in conjunction with the machine learning models 130 to estimate the fair market value of identified farmlands. The machine learning models 130 utilize state-of-the-art artificial intelligence training methodologies, enabling continuous evaluation and improvement over time. These models are designed to analyze thousands of data points, which can be sourced from the geographical database 128 or other databases accessible through the network 114.

[0036] Similarly, the image processing modules 134 are trained to identify and analyze thousands of data points sourced from the geographical database 128 or other accessible databases through the network 114. The platform utilizes the image processing modules 134 to interpret images of the farmland, in addition to the recorded data found in various databases. Users can also upload images of farmland associated with a given address, enabling the platform to perform market value appraisals.

[0037] The cloud-based server 110 analyzes farmland data provided through the search input 124 using machine learning and image processing methodologies. The platform 122 generates an automated farm value appraisal based on this analytical process, delivering the results to the user via the output generator 126.

[0038] The computing device 112 can be any type of client device, including a mobile device, smartphone, handheld computer, tablet, PC, server, or similar device.

[0039] The network 114 can consist of any type of network or a combination of networks, including but not limited to: a wide area network (WAN) such as the internet, a local area network (LAN), a peer-to-peer (P2P) network, a telephone network, private and public networks, packet-switched networks, circuit-switched networks, wired networks, and wireless networks.

[0040] Referring to FIG. 2 and continuing reference to the foregoing figures, an exemplary process, designated as numeral 200, for estimating the fair market value of farmlands is depicted. The process 200 is a computer-implemented method performed within system 100 as shown in FIG. 1.7

[0041] At 201, farmland is identified using an address provided via user input. This address can be entered through one of the many available user interfaces on the farmland appraisal platform, which are accessible on personal or mobile computing devices.

[0042] At 202, the platform retrieves relevant data for the farmland associated with the entered address from a farmland database. The platform uses computer-implemented algorithms to identify and isolate the farmland's boundaries and displays a map representation of the land through a user interface.

[0043] At 203, the platform performs an analysis of the farmland. In one embodiment, this analysis includes soil type and arable acreage.

[0044] At 204, the platform displays comparable farmlands within specified search criteria alongside the originally searched farmland. The comparable farmlands are organized by soil type, arable acreage, and other relevant parameters.

[0045] At 205, the platform uses algorithms to determine adjustment factors for the comparable farmlands. These adjustment factors can then be applied to sort, filter, or manage comparable farmland records within the database.

[0046] At 206, the platform uses machine learning models and image processing technology to analyze the data and estimate the fair market value of the farmland. The fair market value is then displayed through the user interface.

[0047] Referring to FIGS. 3A-3H and continuing reference to the foregoing figures, exemplary user interfaces illustrate the process outlined in FIG. 2.

[0048] In FIG. 3A, a farmland appraisal software interface 300 provides a field for users to enter land identification information. In one embodiment, land can be identified using legal descriptions or parcel numbers. In other embodiments, farmland may be identified by geographical coordinates or other identifiers.

[0049] In FIG. 3B, a graphical representation of the farmland identified by the user is displayed on a map through interface 310. This representation incorporates relevant data for the identified farmland and is overlaid on satellite images of the surrounding area.

[0050] In FIG. 3C, soil details are displayed on interface 320 for the identified farmland.

[0051] In FIG. 3D, details about the arable acreage of the identified farmland are provided on interface 330.

[0052] In FIG. 3E, comparable farmland information is displayed on interface 340 as part of the analysis process. These comparable farmlands are categorized by soil type, arable acreage, and other relevant factors.8

[0053] In FIG. 3F, the platform provides a price estimate for the identified farmland on interface 350. Users can compare the estimated value of the identified farmland with comparable properties for a more informed evaluation.

[0054] In FIGS. 3G and 3H, the platform provides a way for appraisers to input a group of subjects they think should have the same price estimation. Using this method, appraisers can process the group at once, significantly reducing their time compared to doing individual estimations. Exemplary Cloud Architecture

[0055] Referring to FIG. 4 and continuing reference to the foregoing figures, an exemplary cloud architecture, designated as numeral 400, is shown for implementing system 100 (FIG. 1), executing process 200 (FIG. 2), and displaying outputs on interfaces 300-370 (FIGS. 3A-3H).

[0056] The exemplary cloud architecture 400 provides computational, software, data access, and storage services without requiring end-user knowledge of the physical infrastructure. In various embodiments, cloud computing delivers these services over a wide area network, such as the internet, using appropriate protocols.

[0057] Cloud computing providers deliver applications over wide-area networks, which can be accessed via web browsers or other computing components. Software, architectural components, and corresponding data can be stored on servers at remote locations. Cloud computing resources can be consolidated at remote data centers or dispersed, appearing as a single point of access for the user. These components and functions can also be provided from conventional servers or installed directly on client devices.

[0058] Both public and private cloud computing models are included within the scope of this description. Cloud computing provides seamless pooling of resources and reduces the need for end-user management of the underlying hardware infrastructure.

[0059] A public cloud is managed by a vendor and typically supports multiple consumers using the same infrastructure. Unlike a private cloud, a public cloud frees end users from managing hardware. A private cloud, on the other hand, is managed by the organization itself, and its infrastructure is typically not shared with other organizations. However, the organization is still responsible for some hardware management, such as installations and repairs.9

[0060] As shown in FIG. 4, the cloud architecture 400 includes a cloud 410. The cloud 410 (or its various premises) comprises a hardware layer 412, an infrastructure layer 414, a platform layer 416, and an application layer 418.

[0061] A hypervisor 420 manages a set of virtual machines 422, which can include multiple independent virtual machines 424-426. Each virtual machine operates as an isolated software container with its own operating system and applications. The hypervisor 420 decouples each virtual machine from its host server and can dynamically allocate additional virtual machines or shut down existing ones based on workload or processing requirements.

[0062] A variety of client systems 428-430 (such as end-user or administrator systems) can access cloud 410 over a network 432. Depending on the service used by each client system, cloud 410 provides different levels of service. For example, in a software-as-aservice (SaaS) model, the cloud delivers application software and database access. The cloud provider manages the infrastructure and platform layers that run the applications, which are operated at the application layer 418 and accessed by users through client systems 428-430.

[0063] In a platform-as-a-service (PaaS) model, the cloud provider offers services at the platform layer 416, including an operating system, programming language execution environment, database, and web server. Application developers use these platforms to develop and run their software, while the cloud provider manages the underlying hardware and infrastructure.

[0064] In an infrastructure-as-a-service (IaaS) model, the cloud provider delivers physical or virtual machines and other resources as on-demand services. Users install operating system images and application software on the cloud infrastructure 400. The IaaS model provides these resources from large data center pools and supports flexible deployment of user applications. Exemplary Computer System

[0065] Referring now to FIG. 5 and continuing with the foregoing figures, a computer system 500 is shown for implementing an exemplary computing system. This system can be used with system 100 (FIG. 1), process 200 (FIG. 2), or for displaying output on the interfaces 300-370 (FIGS. 3A-3H).

[0066] The hardware architecture of computing system 500 supports the implementation of any of the functional components described herein. Multiple instances10 of computing system 500 may be coupled via one or more networks to perform the described techniques.

[0067] Computing system 500 includes one or more processing devices 510, memory devices 512, communication devices 514, input / output (I / O) devices 516, and mass storage devices 518, all interconnected via interconnect 520. Interconnect 520 may include conductive traces, buses, point-to-point connections, controllers, adapters, or other standard connection devices. Processing devices 510 manage the system's overall operation and may include microprocessors, DSPs, microcontrollers, ASICs, FPGAs, or similar devices.

[0068] Memory devices 512 may consist of RAM, ROM (erasable or programmable), flash memory, hard disk drives, or other suitable storage. Mass storage devices 518 may include hard drives, DVDs, flash memory, or similar devices. Together, these storage components hold data and instructions that configure processing devices 510 to execute the described techniques.

[0069] Communication devices 514 may include Ethernet adapters, cable modems, Wi-Fi adapters, cellular transceivers, baseband processors, Bluetooth transceivers, or combinations thereof. I / O devices 516 may include displays (e.g., touchscreens), speakers, keyboards, mice, microphones, cameras, or other peripherals. For server-only configurations, some I / O devices may not be necessary.

[0070] In client devices, communication devices 514 may include cellular transceivers (e.g., 3G, 4G, 5G), Wi-Fi adapters, Bluetooth transceivers, or combinations thereof. In server devices, communication devices 514 may include Ethernet adapters, DSL modems, or similar equipment to connect to networks.

[0071] A software program or algorithm, when referred to as “implemented in a computer-readable storage medium,” includes computer-readable instructions stored in a memory device (e.g., memory device(s) 512). A processor (e.g., processing device(s) 510) is “configured to execute a software program” when at least one value associated with the software program is stored in a register that is readable by the processor. In some embodiments, routines executed to implement the disclosed techniques can be implemented as part of OS software (e.g., MICROSOFT WINDOWS® and LINUX®) or as part of a specific software application, algorithm component, program, object, module, or sequence of instructions referred to as “computer programs.”

[0072] Computer programs typically comprise one or more instructions stored at various times in various memory devices of a computing device. When read and executed11 by at least one processor (e.g., processing device(s) 510), these instructions cause the computing device to execute functions related to the disclosed techniques. In some embodiments, a carrier containing the aforementioned computer program product is provided. The carrier can be an electronic signal, an optical signal, a radio signal, or a nontransitory computer-readable storage medium (e.g., memory device(s) 512).

[0073] The detailed description provided above in connection with the appended drawings is intended as a description of examples and is not meant to represent the only forms in which the present examples can be constructed or utilized.

[0074] It is to be understood that the configurations and / or approaches described herein are exemplary in nature, and that the described embodiments, implementations, and / or examples are not to be considered limiting, as numerous variations are possible.

[0075] The specific processes or methods described herein can represent one or more of any number of processing strategies. As such, various operations illustrated and / or described can be performed in the sequence illustrated and / or described, in other sequences, in parallel, or omitted. Likewise, the order of the above-described processes can be changed.

[0076] Although the subject matter has been described in language specific to structural features and / or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are presented as example forms of implementing the claims. Supported Features and Embodiments

[0077] The detailed description provided above in connection with the appended drawings explicitly describes and supports various features of a cloud-based platform for farmland market value appraisal. By way of illustration and not limitation, supported embodiments include a computer-implemented program on a cloud computing network, wherein a number of machine learning models and image processing algorithms are incorporated to estimate the market value of farmland.

[0078] Supported embodiments include a computer-implemented method for estimating the fair market value of farmland. The method comprises providing a computer server with a processor and memory, at least one client device with a user interface for displaying information, where the memory comprises a search input, an output generator, a geographical database, a machine learning model, an image processing module, and12 machine learning models; connecting the computer server to the client device over a network; receiving, on the client device, a query relating to the farmland; sending the query, through the network, to the memory input; accessing land information relating to the query from the geographical database; identifying, through the geographical database, at least one comparable farmland; utilizing the machine learning model and the image processing module to analyze at least one data point on the farmland; generating, using the machine learning models, a fair market value output for the farmland; and transmitting the fair market value of the farmland to the client device using the output generator.

[0079] Supported embodiments include a computer-implemented method for estimating the fair market value of farmland through a computer server, wherein the computer server is a cloud server.

[0080] Supported embodiments include a computer-implemented method for estimating the fair market value of farmland through a computer server, wherein the server implements a platform thereon.

[0081] Supported embodiments include a computer-implemented method for estimating the fair market value of farmland through a computer server on the cloud, further comprising determining the soil type and arable acreage of the farmland.

[0082] Supported embodiments include a computer-implemented method for estimating the fair market value of farmland through a computer server, further comprising categorizing at least one comparable farmland by soil type and arable acreage.

[0083] Supported embodiments include a system for estimating the fair market value of farmland. The system comprises a computer server with a processor and memory, where the memory includes a search input, an output generator, a geographical database, a machine learning model, an image processing module, and machine learning models; at least one client device with a user interface for displaying information; and a network connecting the computer server to the client device. The computer server is configured to receive a query about farmland from the client device, transmit the query to the search input, access land information from the geographical database based on the query, identify at least one comparable farmland, utilize the machine learning model and image processing module to analyze at least one data point on the farmland, generate, using machine learning models, a fair market value output for the farmland, and transmit the fair market value of the farmland to the client device using the output generator.

[0084] Supported embodiments include a system for estimating the fair market value of farmland through a computer server, wherein the computer server is a cloud server.13

[0085] Supported embodiments include a system for estimating the fair market value of farmland through a computer server, wherein the server implements a platform thereon.

[0086] Supported embodiments include a system for estimating the fair market value of farmland through a computer server, further comprising determining the soil type and arable acreage of the farmland.

[0087] Supported embodiments include a system for estimating the fair market value of farmland through a computer server, further comprising categorizing at least one comparable farmland by soil type and arable acreage.

[0088] The detailed description provided above in connection with the appended drawings is intended as a description of examples and is not meant to represent the only forms in which the present examples can be constructed or utilized.

[0089] It is to be understood that the configurations and / or approaches described herein are exemplary in nature and that the described embodiments, implementations, and / or examples are not to be considered in a limiting sense, as numerous variations are possible.

[0090] The specific processes or methods described herein can represent one or more of any number of processing strategies. As such, various operations illustrated and / or described can be performed in the sequence illustrated and / or described, in other sequences, in parallel, or omitted. Likewise, the order of the above-described processes can be changed.

[0091] Although the subject matter has been described in language specific to structural features and / or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are presented as example forms of implementing the claims.

[0092] Configuration filtering involves including only farmlands that have the same configuration of the subject property, in terms of arable acres, size, and soil capability. Configuration filters have a significant impact on farmland estimated values. For example, a farmland that is 100% arable has a different price compared to a farmland where only 15% of its size is arable.

[0093] Value variance filtering enables the selection of farmland listings with values within a certain percentage (TO) difference from the calculated value. The TO value would be set differently for each type of land based on size, arability, and soil capability.14

[0094] In an embodiment, the identified comparable properties can be sorted based on numerous keys, including but not limited to, nominal distance, arable acres, and soil capability. The system 100 can deploy nested / multilevel / hierarchical sorting in order of priority, such as nominal distance, arable acres, sale date, and soil capability. Briefly, comparable properties are sorted based on distance, closer to the remoter from the target property. Therefore, the comparable property can be prioritized. Further, comparable properties that were built around the same time as the target property may have higher priority. Comparable properties can be sorted based on the difference in the arable acres they have compared to the target property. Comparable properties with arable acres closer to the target property are given higher priority. The sale date refers to how recently the property was sold. More recently sold farmlands might be considered more relevant for comparison. If two properties are similar in other respects, but one was sold a week ago and the other a month ago, the more recently sold one would be given priority.

[0095] To enable sorting of comparable properties, a minimum number of specific comparable properties (C) would be required. In case the number of comparable properties is smaller than C (#comparables < C), the remote server 106 may relax one or more filters (e.g., spatial filter, temporal filter, configuration filter, and variance filter) to identify more comparable properties.

[0096] The relaxation happens in two dimensions: (a) arable acres: the remote server 106 initiates by including properties with a different arable acreage. For example, if the target farmland has 100% arability, the system 100 may start considering farmlands with less arability as well. (b) distance variance: if relaxing the arable acres filter fails to provide a sufficient number of comparable properties, remote server 106 can consider properties with a wider range of distance. In case the remote server 106 still fails to identify enough comparable properties after relaxing both of the aforementioned filters, remote server 106 expands the search area to include the nearest neighborhood sections. The maximum number of additional neighborhood sections included in the search can be capped at 3 to limit the complexity of the analysis. Alternatively, the user can provide input with regard to the capping count.

[0097] The system 100 ensures that the selected properties closely align with the user's requirements and specific market conditions. Each identified comparable property can be another embodiment; system 100 may estimate value by leveraging the values of the identified comparable properties. The value contribution of each comparable property can be inversely proportional to its distance from the location (provided in the property15 location). In simpler terms, the closer the comparable property is to the property location, the greater its influence on the value. The proximity-driven weighting process ensures that the value reflects the most relevant and immediate market conditions, taking into account not just the characteristics of similar properties but also their geographical relevance. Thus, the estimated value provides a highly precise and localized value estimate, serving as a reliable indicator of the farmland's potential value.

[0098] In an optional embodiment, system 100 can integrate machine learning techniques to process and analyze the multiple comparable properties. The machine learning technique allows the system 100 to perform a self-improvement feedback loop, whereby each performed estimation aids in enhancing its predictive accuracy and operational efficiency. With each successive iteration, system 100 refines the underlying machine learning technique, improving its ability to accurately estimate farmland values. The machine learning technique offers significant advantages. For instance, system 100 can adapt to changing market conditions, trends, and anomalies, continually fine-tuning its approach to deliver reliable, timely, and accurate farmland value estimates. Thus, the application of machine learning in system 100 marks a significant advancement in property valuation technology, pushing the boundaries of precision, scalability, and adaptability in farmland value estimation.

[0099] In another embodiment, system 100 displays the estimated farmland value on the user's computing device 102. The user, therefore, receives a substantiated estimation, meticulously derived from a pool of comparable properties, historic sale prices, and geospatial factors, providing an insightful outlook of the property's potential value.

[00100] In an embodiment, system 100 offers a variety of additional features that cater to different user needs. System 100 could further calculate and display minimum and maximum farmland values. System 100 can also be configured to display a data confidence score, indicating the number of comparable properties used in estimating the farmland value.

[00101] Moreover, system 100 can provide export functionalities, enabling the user to export property information, neighborhood information, satellite images, map data, and street-view photos to a custom report. The user can also visualize a land history and a land improvement value for each comparable property, offering valuable insights into the farmland's past and potential future value.

[00102] In an embodiment, the user can export the custom report to editing software for further modifications or even include their digital signatures in the report before finalizing16 the document. With features like autofilling of farmland characteristics such as arability and soil capability, system 100 significantly enhances the user experience.

[00103] In an embodiment, system 100 leverages machine learning not only for the analysis of data but also for regular updating of the sales database, ensuring real-time accuracy of sales data. The encryption and security measures ensure data integrity and user confidentiality, promoting trust and reliability in system 100.

[00104] In an embodiment, system 100 for estimating the farmland value of the property uses machine learning techniques to analyze the sales database, resulting in highly precise rent range determinations based on the input subject. System 100 identifies comparable properties within a predefined distance range, ensuring the relevance of data for farmland value estimation. By utilizing machine learning, system 100 derives the farmland value, adapting to varied market conditions and property characteristics. The estimated farmland value is presented on the computing device 102, offering an understandable interaction. Efficient data management is achieved via a non-transitory storage device 106-A, ensuring real-time processing and data retrieval. Furthermore, its ability to process subject inputs, such as location, arability, and sales history, lends system 100 versatility and wide applicability in the real estate market, catering to different user needs and scenarios.

[00105] In one embodiment, microprocessor 106-B in system 100 can calculate and present a minimum and maximum farmland value. By utilizing the acquired input subject, system 100 can provide a range of estimated values that are considered reasonable for the specific farmland. Such additional capability allows the user to have a better understanding of farmland value, providing valuable insights for property owners, landlords, or real estate professionals when determining the most appropriate value. System 100, by considering both the lower and upper bounds of the farmland market, enables the user to make informed decisions about the pricing of property.

[00106] In a particular implementation, microprocessor 106-B in system 100 possesses the capability to identify and analyze any number of comparable properties. Such selection is made by considering their recent or historical data within any time frame, say, for instance, up to 365 days. By categorizing these comparable properties, system 100 offers the user a perspective of farmland market trends. By considering a range of comparable properties, users gain insights into the market dynamics and can set a competitive price that aligns with the prevailing trends, ultimately maximizing the property's potential value.17

[00107] In a specific embodiment, microprocessor 106-B of system 100 is equipped with the ability to calculate and present a data confidence score that serves as an indicator of the reliability and accuracy of the estimated farmland value by revealing the number of comparable properties used in the estimation process. By considering the amount of available data, the user may gain insights into the robustness of the estimation. A higher data confidence score suggests a larger pool of comparable properties, enhancing the credibility of the estimation. The confidence score empowers the user to have a better understanding of the level of confidence they can place in the farmland value estimation, aiding them in making informed decisions about their farmland value estimation.

[00108] In yet another embodiment, remote server 106 within system 100 presents a user with a detailed land history and a land improvement value for each comparable property to provide valuable additional insights into the property's past, including noteworthy alterations or enhancements made to the land over time. By understanding land history, the user can better assess the overall value and desirability of the property. The information allows the user to consider factors such as previous land use, renovations, or developments, which can significantly impact a property's appeal and potential farmland value.

[00109] In a specific embodiment, remote server 106 within system 100 utilizes machine learning techniques to determine an optimum comparable property. The algorithms and statistical models analyze vast amounts of data and identify properties that closely match the given criteria for estimating farmland value. By leveraging machine learning, system 100 can learn patterns, trends, and relationships within the data, thereby enabling more reliable and precise estimations, ultimately enhancing the overall accuracy and effectiveness of the farmland value estimation process.

[00110] In a specific embodiment, a sales database within system 100 is maintained and regularly updated to ensure the real-time accuracy of property data. The sales database encompasses vital information, including property addresses, arable acres, soil capability, sale price, sale date, and section. By consistently updating the database, system 100 ensures that users have access to the most current and reliable information.

[00111] In an embodiment, system 100 enables an authenticated user to update the property database by providing an update input to remote server 106. The update input can include details such as changes in property features, arable acres, or soil capability, as well as the addition of new properties. Database updating enables users to access the most up18 to-date and relevant information when estimating market values or conducting property searches, enhancing the overall usability and effectiveness of system 100.

[00112] FIG. 2 illustrates a method 200 for estimating a farmland value of a property, in accordance with an embodiment of the present disclosure. Firstly, the method begins with step 202, receiving a subject input from a user at a computing device 102 (e.g., smartphone, tablet, etc.).

[00113] Once these inputs are received, at step 204, the computing device 102 transmits the dwelling input to remote server 106 through network interface 104, which enables the transmission of data between computing device 102 and remote server 106.

[00114] For the subject input, first, we gather sales records that are within a specific distance from the database, based on data collected in the last 18 months, and then, based on those records and the subject's arability and soil capability, an estimate will be generated.

[00115] The user can also view and modify the calculated parameters for the subject land, which include arability, soil capability, size, and weather history.

Claims

CLAIMS What is claimed is:

1. A computer-implemented method for estimating the fair market value of farmland, the method comprising: providing a computer server having a processor and memory and at least one client device having a user interface for displaying information, wherein the computer server comprises a search input, an output generator, a geographical database, a machine learning model, an image processing module, and machine learning models; connecting the computer server to the client device over a network; receiving, on the client device, a query relating to the farmland; sending, through the network, the query to the memory input; accessing land information relating to the query from the geographical database and weather information using its historical data; identifying, through the geographical database, at least one comparable farmland; utilizing the machine learning model and the image processing module to analyze at least one data point on the farmland; generating, using the machine learning models, a fair market value output for the farmland, and transmitting the fair market value of the farmland to the client device using the output generator.

2. The computer-implemented method of claim 1, wherein the computer server is a cloud server.

3. The computer-implemented method of claim 2, wherein the server implements a platform thereon.

4. The computer-implemented method of claim 1, further comprising determining soil type and arable acreage of the farmland.

5. The computer-implemented method of claim 1, further comprising categorizing the at least one comparable farmland by soil type and arable acreage.

6. A system for estimating the fair market value of farmland, the system comprising:20 a computer server having a processor and memory, wherein the computer server comprises a search input, an output generator, a geographical database, a machine learning model, an image processing module, and machine learning models; at least one client device having a user interface for displaying information, and a network that connects the computer server to the at least one client device, wherein the computer server is configured to receive a query on the farmland from the at least one client device and transmit the query to the search input; wherein the server accesses land information from the geographical database based on the query and identifies at least one comparable farmland, wherein the server utilizes the machine learning model and the image processing module to analyze at least one data point on the farmland, and generates, using the machine learning models, a fair market value output for the farmland, and transmits the fair market value of the farmland to the client device using the output generator.

7. The system of claim 6, wherein the server computes arability using satellite images and an image processing algorithm for the given land query. Additionally, the same is calculated over the sales database at the time of insertion. The server also calculates soil capability for comparables and the query.

8. The system of claim 6, wherein the computer server is a cloud server.

9. The system of claim 8, wherein the server implements a platform thereon.

10. The system of claim 6, wherein the server is configured to determine soil type and arable acreage of the farmland.

11. The system of claim 6, wherein the server is configured to categorize the at least one comparable farmland by soil type and arable acreage.

12. The system of claim 6, wherein the system utilizes weather information from the past 30 years to offer appraisal insights into historical climate conditions and long-term changes.21 13. The system of claim 6, wherein the system utilizes a clustering algorithm to group similar items with co-occurrence patterns. It then calculates an average sale price for each cluster, enabling appraisers to leverage this aggregated data for more efficient valuations.

14. The system of claim 6, wherein the system can handle input in groups, allowing appraisers to process multiple lands simultaneously, thereby reducing the time required for individual appraisals.