Neighborhood housing rent index construction and spatial discontinuity: a machine learning approach
AbstractWe integrate a hedonic housing rent model (econometric approach) into a state-space model (reinforcement machine learning approach). We adopt the kalman filter and smoother recursive algorithm and the expectation maximization algorithm (statistical estimation methods) to estimate the proposed state-space housing rent hedonic model. The method is applied to the Singapore public open rental housing market to construct housing rent indexes. Compared with the conventional econometric methods in index construction, the proposed model has three advantages. Firstly, a state-space modeling approach technically allows us to construct neighborhood level housing rent indexes through a reinforcement learning process regardless the sample size in a neighborhood. Secondly, the expectation maximization algorithm effectively enhances the robustness of maximum likelihood estimation for a dataset being repleted with unobservable information, for example, fewer or zero transactions in certain time periods. Thirdly Kalman filter and smoother recursive algorithm optimizes the estimates by capturing all information (before and after a time point) to predict a housing rent at a time point. This helps reduce the bias caused by sticky rents. The paper empirically proves that the proposed model outperforms other types of index models in prediction accuracy, hence produces more accurate housnig rent indexes at neighborhood level.
Accurately constructing neighborhood housing rent indexes are impotant in real estate valuation, real estate investment returns and risk analyses. This is because the spatial patterns of housing price distribution may change over time, which is resulted from urban developments. To illustrate it, we apply K-shape clustering algorithm in unsupervised machine learning literature to the neighborhood housing rent indexes to analyze the dynamic patterns of the spatial distribution of housing rents. We find the spatial discontinuity of housing rent dynamics. The housing rent indexes in some spatially disconnected neighborhoods appear to have similar dynamic pattern, while different dynamic patterns are found in some spatially adjacent neighbothoods.