Abstract: Accurate mapping of the fine-scale spatial pattern of urban housing rent is crucial to study the urban residential behavior and to improve the rationality of urban planning. This paper proposed the use of online rental data as a reliable new data source for mapping a housing rent spatial pattern. Utilizing individual rental information from one of the biggest online house trading and renting platforms, Anjuke, we attempted to map the urban housing rent of the community unit, which is the most precise administration unit in China. Due to the online rental data not being evenly distributed throughout the region, we conducted a neural network-based hedonic model to predict the housing rent of unknown community units with six evaluation indicators: environmental conditions, traffic systems, job opportunities, living facilities, education, and health care. The 2015 housing rental market of Shenzhen was chosen as a study case. The prediction error (% RMSE) of the hedonic model-which is trained with online rental data-is 13.87%. The mapping result is consistent with the real spatial pattern of housing rent, and the average rent across the whole study area is 60.56 yuan/m2, 5.69 yuan/m2 less than the official statistical average. The case study results indicated that using online rental data can effectively map the spatial pattern of housing rent on a community scale. There are three significant benefits of using this online data to build the model: First, the proposed online data-based rent mapping method can be applied universally, as all the data as well as the model used in the proposed mapping method are generally open-access. This means that other researchers can easily use this method to map the housing rent or prices of a different study area. Second, there is a much larger volume of online rental data than what can be obtained through survey samples or real rental samples. Furthermore, the data collection cost is much lower than that of traditional data sources. Third, the update cycle of online rental data is more frequent, meaning that the urban rental market can be monitored at a high spatial-temporal resolution if this online data-based method is used. The outcomes of utilizing this method can also supply fundamental data concerning urban social issues studies such as housing burden, urban poverty, and residential segmentation.

Key words: community unit rent, internet open data, Anjuke, feed-forward neural network, Points of Interest, Shenzhen City