Tracking the Pulse of a City – 3D Real Estate Price Heat Maps

Representative price indices are required to understand the determinants of city growth and to formulate effective policies that support sustainable housing markets and stabilize price trends. Current practices are challenged simultaneously by spatial heterogeneity and spatial autocorrelation effects, which may produce errors in analyzing the regional dynamics of housing prices. This paper proposes the use of a semi-parametric model based on a locally weighted approach that controls for dynamic agglomeration and diffusion effects. Based on a residential transaction dataset in Singapore, we create the three-dimensional interactive heat maps that allow better visualization of regional variations and heterogeneity in price appreciation trends. The time-varying price changes show clearly the existence of regional variations and temporal dispersions in price appreciation rates in the sample periods. There are discernible dynamic patterns during the boom and the bust periods. The central regions experience more moderate price increases during the boom periods. Our methodology provides high-resolution information on price dynamics that can aid individual buyers, investors, and policy makers in making objective and informed decisions.