Real estate valuation-linked deep learning-based interior improvement solution

Abstract

The present disclosure relates to a deep learning-based interior improvement solution linked to real estate valuation. An interior improvement solution system according to one embodiment of the present disclosure may include a data collection module, a deep learning-based analysis module, an interior improvement recommendation module, a value increase measurement module, and a user interface.

Description

Technology Field

[0001] The present disclosure relates to a deep learning-based interior improvement solution linked to real estate valuation, and more specifically, to interior improvement using deep learning technology to increase real estate value and the provision of a valuation report based thereon. Background Technology

[0002] Real estate value is generally determined by physical factors such as a building's location, age, and area. These factors serve as primary criteria for valuation in the real estate market and are based on relatively objective data. However, despite the recent increase in interest regarding the impact of interior improvements on real estate value, there is a lack of systematic methods to quantitatively evaluate this effect. This reflects the reality that, while interior improvements are a significant factor in altering the external and functional value of real estate, it is difficult to clearly quantify or analyze their effects.

[0003] Under these circumstances, landlords and real estate agents consider interior improvements to maximize property value, but they struggle to find the optimal solution. In particular, achieving cost-effective improvements within a limited budget presents an even more complex challenge. This is due to a lack of tools capable of accurately predicting or analyzing the impact of various interior elements on property value. Consequently, landlords and agents are forced to rely on subjective judgment when selecting improvement plans, which increases the likelihood of inefficient outcomes.

[0004] Furthermore, this issue can have a negative impact on the overall efficiency of the real estate market. If market participants do not clearly understand the effect of interior improvements on property value, they are more likely to make incorrect investment decisions. This prevents the full utilization of the potential for property value appreciation and, consequently, can hinder the growth and development of the market. Therefore, the lack of a systematic method to analyze and evaluate the impact of interior improvements on property value is emerging as a significant issue in the current real estate market.

[0005] The purpose of the present disclosure is to provide a real estate valuation-linked deep learning-based interior improvement solution that provides reasonable and efficient value to both landlords and tenants through interior improvement based on deep learning technology linked to real estate valuation. means of solving the problem

[0007] In one embodiment of the present disclosure, an interior improvement solution system may be provided. The interior improvement solution system may include a data collection module, a deep learning-based analysis module, an interior improvement recommendation module, a value increase measurement module, and a user interface.

[0008] In one embodiment of the present disclosure, the deep learning-based analysis module may include an AI model that selects undervalued real estate by learning data with weighted real estate interior elements.

[0009] In one embodiment of the present disclosure, the value increase measurement module may include an AI model that learns the valuation factors of real estate to derive a future increase in value.

[0010] In one embodiment of the present disclosure, the interior improvement recommendation module may include an AI model that learns internal and external elements of real estate to derive an optimal interior improvement plan.

[0011] In one embodiment of the present disclosure, a user interface may be provided so that a user can view interior improvement proposals and analyze expected value increase effects through a web or mobile application. Effects of the invention

[0012] According to one embodiment of the present disclosure, by analyzing data through a deep learning-based analysis module, a rental business operator and a real estate agent can efficiently derive an optimal interior improvement plan. According to one embodiment of the present disclosure, by utilizing an interior improvement recommendation module, a cost-effective increase in value can be realized. Brief explanation of the drawing

[0013] Figure 1 is a diagram illustrating the configuration module of an interior solution system. Specific details for implementing the invention

[0014] This is a diagram illustrating the configuration modules of an interior solution system.

[0015] Referring to FIG. 1, the interior solution server (100) is a system capable of providing a deep learning-based interior improvement solution linked to real estate valuation. The interior solution server (100) can collect, analyze, recommend, and measure data to derive an optimal interior improvement plan for increasing real estate value. The interior solution server (100) may be composed of a data collection module (110), a deep learning-based analysis module (120), an interior improvement recommendation module (130), a value increase measurement module (140), and a database (150).

[0016] The data collection module (110) can collect real estate-related data. The data collection module (110) can store the collected data in a database (150) so that it can be analyzed by the deep learning-based analysis module (120). The data collection module (110) can utilize various data sources to increase the efficiency of the deep learning-based interior improvement solution linked to real estate valuation. The data collection module (110) may include a real-time data update and verification process to ensure the accuracy and reliability of the data.

[0017] In one embodiment of the present disclosure, the data collection module (110) can collect environmental data, such as temperature, humidity, and lighting conditions inside a building in real time by utilizing IoT (Internet of Things) sensors. The data collection module (110) can provide additional information necessary for interior improvement plans based on this environmental data. Additionally, the data collection module (110) can automatically collect data provided by real estate-related online platforms by utilizing web crawling technology. The data collection module (110) can refine and classify the collected data to prepare it for use in the deep learning-based analysis module (120). Data directly entered by the user through a user interface can also be collected, and this can be integrated with other data sources to increase the accuracy of the analysis.

[0018] The deep learning-based analysis module (120) can analyze data collected from the data collection module (110) to derive information necessary for interior improvement. The deep learning-based analysis module (120) processes data using deep learning technology and can provide information necessary for deriving interior improvement plans based on the analysis results. The deep learning-based analysis module (120) can analyze the current state of the real estate through a deep learning algorithm and identify elements that need improvement. The analysis results are provided to the interior improvement recommendation module (130).

[0019] In one embodiment of the present disclosure, the deep learning-based analysis module (120) may include an AI model that selects undervalued real estate by learning data with weighted real estate interior elements. The deep learning-based analysis module (120) may evaluate the potential for an increase in the value of real estate based on the learned data and derive elements that need improvement. Additionally, it may include an AI model that derives an optimal interior improvement plan by learning internal and external elements of real estate. The deep learning-based analysis module (120) may identify specific elements that need improvement by analyzing the structural characteristics, location, surrounding environment, market trends, etc. of the real estate.

[0020] The interior improvement recommendation module (130) can recommend optimal interior improvement plans based on the analysis results provided by the deep learning-based analysis module (120). The interior improvement recommendation module (130) may include an AI model that learns internal and external elements of the real estate to derive improvement plans. For example, it may include color combinations, space utilization, lighting placement, furniture placement, material selection, styling options, maintenance plans, etc. The interior improvement recommendation module (130) can transmit the derived improvement plans to the value increase measurement module (140) to enable the evaluation of the effect of increasing the value of the real estate.

[0021] In one embodiment of the present disclosure, the interior improvement recommendation module (130) may be provided so that a user can view and select improvement plans through a user interface. Detailed information on improvement plans may be visually provided to the user through a web or mobile application, and the recommendation algorithm may be continuously improved by collecting user feedback. Additionally, customized improvement plans may be proposed by reflecting specific regional characteristics and market trends.

[0022] The value increase measurement module (140) can measure the effect of real estate value increase according to the improvement plan provided by the interior improvement recommendation module (130). The value increase measurement module (140) can store the measured results in a database (150) so that they can be used for subsequent analysis and report generation. By utilizing deep learning technology, data related to internal and external factors of real estate can be learned, and the effect of value increase can be quantitatively evaluated based on this.

[0023] In one embodiment of the present disclosure, the value increase measurement module (140) can predict the possibility of a real estate value increase in a specific area through an AI model that learns real estate valuation factors. In addition, it can contribute to selecting the optimal improvement plan by analyzing the cost-effectiveness of interior improvement plans. By evaluating the economic efficiency of improvement plans, the value increase measurement module (140) can support rational decision-making for the user.

[0024] The database (150) can store data generated from the data collection module (110), the deep learning-based analysis module (120), the interior improvement recommendation module (130), and the value increase measurement module (140). The database (150) can provide data required by each module, including real estate DB and valuation DB. The database (150) can provide data to a user interface through a web or mobile application. The database (150) can deliver necessary data to the user interface so that the user can check interior improvement plans and analyze the expected value increase effect. Additionally, it can store and manage data for training AI models, and can maintain and update the quality of the data to improve the performance of the AI ​​model. Explanation of the symbols

[0025] 100: Interior Solution Server 110: Data Collection Module 120: Deep learning-based analysis module 130: Interior Improvement Recommendation Module 140: Value Increase Measurement Module 150: Database

Claims

Claim 1 An interior improvement solution system based on deep learning linked to real estate valuation, characterized by comprising: a data collection module; a deep learning-based analysis module; an interior improvement recommendation module; a value increase measurement module; and a user interface. Claim 2 An interior improvement solution system according to claim 1, wherein the deep learning-based analysis module includes an AI model that selects undervalued real estate by learning data with weighted real estate interior elements. Claim 3 An interior improvement solution system according to claim 1, wherein the value increase measurement module comprises an AI model that learns real estate valuation factors and derives a future increase valuation rate. Claim 4 An interior improvement solution system according to claim 1, wherein the interior improvement recommendation module comprises an AI model that learns internal and external elements of real estate to derive an optimal interior improvement plan. Claim 5 An interior improvement solution system according to claim 1, characterized in that the user interface provides a user with the ability to view interior improvement plans and analyze expected value increase effects through a web or mobile application.

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