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Utilization of Machine Learning, Government-Based and Non-Conventional Indicators for Property Value Prediction in the Philippines

Year: 2023       Vol.: 72       No.: 1      

Authors: Gabriel Isaac L. Ramolete, Bryan Bramaskara, Dustin A. Reyes, and Adrienne Heinrich


Property appraisal and value estimation in the Philippines are prone to human errors and bias, due to price subjectivity and the general difficulty in properly quantifying the impact of factors beyond the property itself. Predictive models for property valuation typically involve conventional features of the house (e.g., number of bathrooms) and market prices of nearby properties. This paper investigates the value of incorporating alternative data to account for deviations in true market value and improve property value predictions in the Philippines and other developing countries. The study considers public data and anchors socio-economic indicators to assess its relevance to property value prediction in the Philippines. By utilizing the Department of Trade and Industry’s 2021 National Competitiveness Index Rating, this research also investigates the significance of a Local Government Unit’s competitiveness based on their economic dynamism, government efficiency, infrastructure, and resiliency. Different commonly used Machine Learning (ML) methods and features from various data sources are compared and it is found that the inclusion of government indicators has substantial positive effect on the model performance on top of conventional indicators that can be globally replicated. A Mean Average Percentage Error (MAPE) of 10.7-21% is obtained which is competitive compared to the performance ranges of other reported models. A property segment (personalized) approach is proposed to achieve lower error rates in Philippine appraisal (in 87.5% of cases), better access and transparency for populations outside the real estate network, and minimally biased assessments, all of which are also relevant for other developing countries.

Keywords: property appraisal, spatial analysis, city competitiveness, clustering

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