Available literature claims that location is a key attribute in the housing market. However, the impact of this attribute is difficult to measure and the traditional hedonic approach using subjective assessments is problematic. This paper seeks to explore trend surface analysis technique, attempting to provide an alternative way to measure location values.
TSA works in a similar way to other response surface methods but it is implemented directly in regression models, using a set of combinations of the co‐ordinates of properties in several power degrees. It can also be implemented in artificial neural networks, taking advantage of the neural ability in non‐linear domains. This work presents a comparison between traditional regression approach, error modelling, response surfaces, and TSA. ANN is also used to estimate some models, comparing their results. The objective is to verify the behaviour of TSA in hedonic models. A case study was carried using data of over 30,000 sales tax data of apartments sold in Porto Alegre, a southern Brazilian town.
The results indicates that TSA is an effective tool for the spatial analysis of real estate, because TSA models are similar to other approaches, but are developed with less expert work.
This paper presents an application of TSA in real estate market, which is an interesting alternative to traditional measures of location attributes.
Aurélio Stumpf González, M., Soibelman, L. and Torres Formoso, C. (2005), "A new approach to spatial analysis in CAMA", Property Management, Vol. 23 No. 5, pp. 312-327. https://doi.org/10.1108/02637470510631474Download as .RIS
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