01659682

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Linear regression with Ordinary Least Squares and spatial econometric models are statistical methods widely employed to measure the impact of transportation infrastructure locations on real estate prices. Efthymiou and Antoniou (1, 2, 3) developed different types of ordinary least squares (OLS) and spatial econometric models to measure the impact of transportation infrastructure and policies on purchase and rent prices of dwellings, using data downloaded from publicly available on line sources. They found that spatial econometric models perform better than OLS in terms of model fit and detection of spatial autocorrelation, resulting in lower AIC and Moran’s I. In this research the authors investigate the potential of using Machine Learning (ML) models to measure transportation cost capitalization on real estate prices, and benchmark their results versus spatial econometric models and OLS. They develop different types of random forest and gradient boosting machine models using hyperparameter optimization and stacked ensemble learning. The results show that ML models outperform traditional statistical techniques in terms of model fit (e.g. lower MSE) and successfully resolve heteroscedasticity between predicted and observed values. Moreover, it is shown that transformation of independent variables does not improve the performance of ML models. However, the output of ML models cannot be interpreted in a similar manner as that of statistical models, meaning that the elasticities and percentage impact of transportation infrastructure on real estate prices cannot be directly computed.

This paper was sponsored by TRB committee ABJ70 Standing Committee on Artificial Intelligence and Advanced Computing Applications. Alternate title: Machine Learning Versus Spatial Econometric Models: Modeling the Impact of Transportation Infrastructure on Real Estate Prices

18-01718

English

19p

2018

Transportation Research Board 97th Annual Meeting

Digital/other

Figures; References; Tables

Econometric models; Infrastructure; Machine learning; Prices; Property values; Real property; Spatial analysis

Greece

Data and Information Technology; Economics; Finance; Planning and Forecasting; Policy; Transportation (General)

Transportation Research Board Annual Meeting 2018 Paper #18-01718

TRIS, TRB, ATRI

Jan 8 2018 10:26AM