Predicting Residential Property Value in Catonsville, Maryland: A Comparison of Multiple Regression Techniques

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Lee Whieldon and Huthaifa Ashqar, Predicting Residential Property Value in Catonsville, Maryland: A Comparison of Multiple Regression Techniques, https://arxiv.org/abs/2101.01531

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Abstract

This study aims to predict the prices of residential properties in Catonsville, MD based on publicly available tax assessment data maintained by Maryland government institutions. Recent legislation in Maryland has made it a requirement for local governments to provide up-to-date, easily accessible data to its constituents. This study explores predicting residential property prices in Catonsville, MD using three regression techniques: Linear, Ridge, and Lasso regression. Catonsville, MD was chosen as a case study since it is gaining a lot of interest as a suburb to Baltimore City, MD and is considered one of the most competitive markets for house sales. Extracting over 11,000 property records from Maryland’s Open Data Portal (ODP), we transformed the data locally and applied regression techniques to predict the price. We used various independent features to predict the price of residential properties including prior year housing sales price, size of house, house age, street address type (single family or townhouse), if the house has a basement, dwelling type (center unit, end unit, split level, or standard unit), number of stories, and dwelling grade (scale from 1 to 6). Outperforming Linear and Ridge regression, Lasso regression can enhance the predictability of housing prices and significantly contribute to the correct evaluation of real estate price using only two years of historical data and without additional socioeconomic variables. We also found that among others; prior year housing sales price (positive), size of house (negative), and house age (positive) are the most significant factors in predicting housing sales price in Catonsville, MD.