Abstract
Market information on housing is relevant for good decision making of households as well as for real estate economics and is also of systemic interest. The official statistics often provide only annual indices on national level based on transactions of previous year. The stakeholders of the real estate markets however request analysis of higher spatial resolution on local level based recent transactions. This paper focuses on methodology of automated data acquisition, analysis and visualization of data from the housing market. Data retrieved from observing real estate markets (to rent/to buy) are used for statistical modelling of value-descriptive parameters, for estimating and forecasting real estate market fundamentals, which also can reveal market risks. This paper elaborates methods of automated data acquisition based on web mining, reviews methods of econometric spatial modelling with impact from proximity in order to deduct efficiency parameters within different categories. The analysis and the resulting visualization at different granularity of spatial, temporal and typological effects provides relevant information for decision making.
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Notes
- 1.
KDD: Knowledge Discovery Databases process model (Fayyad et al. 1996: 41).
- 2.
CRISP-DM: CRoss Industry Standard Process for Data Mining.
- 3.
SEMMA: Sample, Explore, Modify, Model, Assess.
- 4.
ETL: Extract from data source—Transform into proper format—Load into target database.
- 5.
- 6.
EU-Directives 2002/91/EG and 2010/31/EU.
- 7.
Benchmarks such as Global Real Estate Transparency Index and Global Real Estate Bubble Index. Information provided by stakeholders of the transfer market might be unbiased.
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Muggenhuber, G. (2019). Geospatial Data Mining and Analytics for Real-Estate Applications. In: Döllner, J., Jobst, M., Schmitz, P. (eds) Service-Oriented Mapping. Lecture Notes in Geoinformation and Cartography. Springer, Cham. https://doi.org/10.1007/978-3-319-72434-8_11
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