What Predicts Real Estate Price Better: Closeness in Geographic or Characteristic Space?

Student: Kusakin Danil

Supervisor: Evgeniy M. Ozhegov

Faculty: Faculty of Economics, Management, and Business Informatics

Educational Programme: Economics (Bachelor)

Final Grade: 9

Year of Graduation: 2018

The project is aimed to study the residential property sellers’ behavior on secondary real estate market. It is widely known that when selling a real estate object, a seller takes into account the objects characteristics, characteristics of surroundings and the current average market price of alternatives. In this research we extend spatial autoregression (SAR) model that allows us to study the effect of closeness on a price not only in a geographical space, but in a characteristic space as well. Results of estimation demonstrate that proximity in characteristic space has a greater effect on a real estate price than a geographical closeness; it was also found out that simultaneous inclusion of weighting matrices based on a geographical and characteristic space into the spatial model increases predictive power of the model.

Full text (added May 18, 2018)

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