A Hybrid Approach for Mass Valuation of Residential Properties through Geographic Information Systems and Machine Learning Integration

Abstract

Geographic Information Systems (GIS) and Machine Learning methods are now widely used in mass property valuation using the physical attributes of properties. However, locational criteria, such as as proximity to important places, sea or forest views, flat topography are just some of the spatial factors that affect property values and, to date, these have been insufficiently used as part of the valuation process. In this study, a hybrid approach is developed by integrating GIS and Machine Learning for mass valuation of residential properties. GIS‐based Nominal Valuation Method was applied to carry out proximity, terrain, and visibility analyses using Ordnance Survey and OpenStreetMap data, than land value map of Great Britain was produced. Spatial criteria scores obtained from the GIS analyses were included in the price prediction process in which global and spatially clustered local regression models are built for England and Wales using Price Paid Data and Energy Performance Certificates data. Results showed that adding locational factors to the property price data and applying a novel nominally weighted spatial clustering algorithm for creating a local regression increased the prediction accuracy by about 45%. It also demonstrated that Random Forest was the most accurate ensemble model.

Real Estate Industry Sustainable Solution (Environmental, Social, and Governance) Significance Assessment—AI-Powered Algorithm Implementation

“…One of the key applications of AI in sustainable real estate development lies in property valuation and is strongly connected with Automated Valuation Modeling (AVM), Mass Appraisal (MA), or computer-assisted Mass Appraisal (CAMA) [37,38]. The simultaneous utilization of Geographic Information Systems and machine learning for that purpose has also been thoroughly investigated in [39] and found its application. AI-driven algorithms adeptly analyze a multidimensional variable describing market trends.…”

House Type Specification for Housing Development Project Using Machine Learning Techniques: A Study From Bangkok Metropolitan Region, Thailand

“…Regarding the literature review, the proximity to places of employment or essential places (e.g. shopping malls, mass transit stations, and utilities) significantly influences housing projects in a specific location (Acheampong, 2018;Khan et al, 2017;Mete & Yomralioglu, 2022). In this study, therefore, two groups of factors were used to determine the characteristics of the locations:…”

A Comprehensive Overview Regarding the Impact of GIS on Property Valuation

“…With the increase of public and open-source geospatial data GISs, machine learning methods can be improved. Mete and Yomralioglu [15] used open data sources from the European Environment Agency, OpenStreetMap, and Ordnance Survey to perform a mass assessment of residential properties in England and Wales using a hybrid approach combining GISs and machine learning regression algorithms. In this study, the results of different regression methods were compared and the random forest algorithm was found to have higher accuracy than other algorithms.…”

“…For example, some researchers have investigated various methods to automate the valuation process by combining geospatial methods with mathematical methods based on fuzzy logic [6][7][8][9], interpolation [10,11], or statistical methods. Other researchers have studied the combination of GIS with artificial intelligence, neural networks [12][13][14], or machine learning [15]. Another automatic evaluation method studied is the combination of the hedonic model with spatial analysis based on GIS [16,17].…”

“…Some examined how sustainable infrastructure and amenities affect property values by contributing to a higher quality of life and attractiveness of a location [24]. Others explored whether GIS can also assess the potential impact of renewable energy projects on the environment and neighboring communities, and on the value of properties affected by renewable energy projects [15]. The effects of accessibility and mobility on property value have also been studied [25,26].…”