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PREDICTING HOUSE PRICES USING BOTH GEOGRAPHICALLY WEIGHTED REGRESSION AND KRIGING METHODS
Abstract
The problem of estimating the price of residential real estate is considered. For its solution it is proposed to use hedonic house price models such as local spatial models: geographically weighted regression and kriging. They attempt to explain variation in house prices using property structural and location characteristics. To improve the quality of construction of a variogram model, it is proposed to use robust methods. Methods are compared based on real data on the house prices in the city of Novosibirsk. The advantages and disadvantages of each method are identified. The result is a hedonic price model that describes the impact on the price of housing characteristics: total apartment area, living area, kitchen area, floor, indicator that the apartment is located on the ground floor, indicator that the apartment is located on the first floor, indicator that the apartment is located on the top floor, number of storeys, housing material. The obtained models can help sellers and buyers of real estate to determine market-based prices.
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