Automated valuation model for residential rental markets: evidence from Japan

We introduce a new type of automated valuation model (AVM) for residential rental markets employing the ordinary kriging method. Using nearly 300, 000 coordinates of individual properties and a proprietary dataset of asking rental prices, we form a unique micro-level housing rental dataset for five major metropolitan areas in Tokyo, Japan, and estimate the rental AVM with kriging, utilising only latitude and longitude. From our training and test datasets, we find that the accuracy of the ordinary kriging method is comparable to the traditional hedonic pricing approach, which requires substantial property information. Our finding suggests that the efficiency of the ordinary kriging approach for rental AVM is comparable to the hedonic pricing approach. For robustness, we investigate the roles of spatial variables based on our baseline hedonic regression models. Spatial variables—latitudes, longitudes, and distance to Tokyo Station—are significant in determining housing rents in the Tokyo residential market. By providing an open-source AVM for the residential rental market, we alleviate the information asymmetry between the tenants-to-be and property owners and increase the efficiency of housing markets.