The effect of zip-code image and renovation on the prices of apartments in old residential multi-dwelling buildings
||Mikael Postila and Ari Laitala
||The effect of zip-code image and renovation on the prices of apartments in old residential multi-dwelling buildings
||19th Annual European Real Estate Society Conference in Edinburgh, Scotland
||In a recent research paper it has been claimed, that the plans nor the completion of major renovations won’t affect fully the sales prices on the market in Finland. Our goal in this study is twofold. Firstly, our intention is to check whether the discrepancy between renovation costs and apartment prices really exists. Secondly, we try to quantify the renovation potential for various sub-markets. As the models are planned to be used also for real life decision making, multivariate regression model is chosen for the tool of choice. Even though it isn’t as efficient as some spatial econometric tools, it is understandable for most practitioners, who often have doubts on models they don’t understand and see as ‘black boxes’ from their point of view. The dataset used for the study is comprised from Oikotie.fi multiple listing service and Statistics Finland data. During the first phase of the study we have constructed a multivariate hedonic regression models using same variables and similar functional form for 40 urban regions in Finland. One of the explanatory variables is quality, which is represented by using 3 dummy variables. The elasticity varies between cities, larger and more expensive cities having smaller elasticity. Since the renovation costs are somewhat similar across the country this indicates that the monetary difference between the classes of different building and apartment quality levels would be roughly the same. In the second stage of the study we’re trying to verify the results by replicating the 1st stage research on zip-code level and for individual residential buildings. At the same time we are testing the explanatory power of various zip-code, or sub-zip-code specific socio-economic and demographic variables as well as image (e.g. security, service level perception) factors.
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||Parallel Session A7
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