Establishing Dynamic Impact Function for House Pricing Based on Surrending Multi-Attributes: Evidence From Taipei City, Taiwan

Abstract

The objective of the research is aimed for a solution that is to establish the dynamic impact function of surrounding multi-attribute for house pricing. It is also able to measure the ripple effect and allows the hedonic parameter estimates to vary from point-to-point. A comprehensive literature review is carried out to obtain an adequate theoretical basis for the corresponding hypothesis and concepts. The proposed dynamic impact function for multi- attributes is then constructed based on the characteristics of surrounding facilities. Adopting the convenience sampling criteria of 95% confidence level on the data sampling and 10% limit of error in a 5−95% proportion, we collect the empirical data of 39 yearly house sales in the investigated urban areas of Taipei city focusing on housing prices and then utilize them for evaluating and adjusting the function. The actual house price and that of proposed function affected by Mass Rapid Transit (MRT) stations are analysed, resulting in the correlation coefficient at 0.946 (single attribute) and 0.944 (multi-attribute), respectively. The findings support that proposed function can highly represent the house pricing pattern and be an accurate tool for appraisers.

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“…The multiple-criteria decision-making analysis (MCDA) (Antucheviciene et al, 2015) is frequently involved in property valuation and price prediction. The most commonly used parameters (criteria) for the property valuation and price prediction defined by IVS and authors in their articles are building age, the number of rooms, floor area, lot size, property location, view, distance to the points of interest such as public transport, shops, subway (Sirmans et al, 2005;Ja'afar et al, 2021;Chen et al, 2020). The location of the building is considered a parameter that has a strong influence on the price of the premise (Schläpfer et al, 2015).…”