Towards a data-rich infrastructure for housing-market research: deriving floor-area estimates for individual properties from secondary data sources
Recent years have witnessed substantial advances in the precision and availability of digital infrastructure data, remote sensing data, and microscale socioeconomic data for urban areas in many parts of the world. However, these data still remain deficient in detail especially with respect to the fine-grained property-level structural attributes that form the basis of housing-market models and simulations. This lack of detail is hindering the development of house-price models, the validation of housing-market theories, and restricting the quality of empirical input into urban planning. A methodology is developed for deriving estimates of floor area for individual properties from the integration of Ordnance Survey Mastermap data and Environment Agency LiDAR data for an area in the city of Cardiff, Wales. Floor area is an important determinant of house price and a fundamental variable in housing-market models. The estimates are validated against measures of floor area obtained from an estate-agent survey of a large sample of properties. The research investigates the reasons for the differences in the estimates and the implications for the methodology. The paper concludes with a discussion about how the methodology can be developed and some future research applications.
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