The Impact of Online Real Estate Listing Data on the Transparency of the Real Estate Market - Using the Example of Vacancy Rates

Despite the increasing digitization of the real estate market and the accompanying greater availability of data, as evidenced, for example, by the proliferation of online real estate listing platforms, there are still deficiencies in market transparency associated with a variety of negative aspects. This study aimed to investigate the impact of online real estate listing data on market transparency by examining the suitability of these data for scientific use in general and for the example of estimating vacancy rates in particular. Therefore, a comprehensive data set consisting of more than seven million listings was collected over one and a half years and analyzed with regard to all available features in terms of their quality and quantity. Furthermore, their explanatory power for estimating vacancy rates was tested by their application in different regression models. The features specified in online real estate listings showed an average completeness of 85.97 % and, most widely, plausible feature specifications. Exceptions were information regarding energy demand, which were only available in 20.79 % of listings, and the specification of the building quality and condition, which showed indications of being positively biased. The estimation of vacancy rates on the district level, based on online real estate listing data, showed promising results, being able to explain vacancy rates with a goodness of fit of a pseudo R² of 0.81 and a mean absolute error of 0.84 percentage points. These results suggest that information contained in online real estate listing data are a good basis for scientific evaluation and are specifically well suited for estimating vacancy rates. The findings imply the utilization of online real estate listing data for a diverse range of purposes, extending beyond the current focus of price-related research

Modeling the Census Tract Level Housing Vacancy Rate with the Jilin1-03 Satellite and Other Geospatial Data

The vacant house is an essential phenomenon of urban decay and population loss. Exploration of the correlations between housing vacancy and some socio-environmental factors is conducive to understanding the mechanism of urban shrinking and revitalization. In recent years, rapidly developing night-time remote sensing, which has the ability to detect artificial lights, has been widely applied in applications associated with human activities. Current night-time remote sensing studies on housing vacancy rates are limited by the coarse spatial resolution of data. The launch of the Jilin1-03 satellite, which carried a high spatial resolution (HSR) night-time imaging camera, provides a new supportive data source. In this paper, we examined this new high spatial resolution night-time light dataset in housing vacancy rate estimation. Specifically, a stepwise multivariable linear regression model was engaged to estimate the housing vacancy rate at a very fine scale, the census tract level. Three types of variables derived from geospatial data and night-time image represent the physical environment, landuse (LU) structure, and human activities, respectively. The linear regression models were constructed and analyzed. The analysis results show that (1) the HVRs estimating model using the Jilin1-03 satellite and other ancillary geospatial data fits well with the Census statistical data (adjusted R2 = 0.656, predicted R2 = 0.603, RMSE = 0.046) and thus is a valid estimation model; (2) the Jilin1-03 satellite night-time data contributed a 28% (from 0.510 to 0.656) fitting accuracy increase and a 68% (from 0.359 to 0.603) predicting accuracy increase in the estimate model of the housing vacancy rate. Reflecting socio-economic conditions, the luminous intensity of commercial areas derived from the Jilin1-03 satellite is the most influential variable to housing vacancy. Land use structure indirectly and partially demonstrated that the social environment factors in the community have strong correlations with residential vacancy. Moreover, the physical environment factor, which depicts vegetation conditions in the residential areas, is also a significant indicator of housing vacancy. In conclusion, the emergence of HSR night light data opens a new door to future microscopic scale study within cities