Three of these enhancements-regression kriging, mixed regressive-spatial autoregressive, and geographically weighted regression-are widely utilized spatial econometric models. Specifically, house value estimates were obtained by combining predictions from repeat sales and various hedonic regression specifications, which were enhanced to account for spatial effects. While the merits of combining these models when constructing house price indices are well documented, research on the utility of adopting the same approach for residential property valuation has not been conducted to date. Hedonic regression and repeat sales are commonly used methods in real estate analysis. Empirically, using a novel criterion, we show that in higher frequency comparisons, our hedonic method outperforms competing alternatives. In addition, the state-space representation of the model includes a geospatial spline surface which significantly reduces the number of parameters to be estimated when compared to the standard practice of including postcode dummies in the model. In such cases, the reliability of the hedonic imputation method is improved by using a state-space formulation which yields estimates of the shadow prices that are weighted sums of previous periods’ information. The problem is that at higher frequencies, there may not be enough observations per period to reliably estimate the characteristic shadow prices. We use a spatio-temporal model to improve the method’s effectiveness on housing data at higher frequencies. These shadow prices are used to construct matched samples of predicted prices, which are inserted into standard price index formulas. ![]() The hedonic imputation method allows characteristic shadow prices to evolve over time. The modification can be viewed as a general nonparametric method for estimating a function of two variables. The spatial coordinate model used in the present paper is a modification of Colwell’s (1998) spatial interpolation method. The present paper addresses the Hill and Scholz question in the context of providing satisfactory residential land price indexes. To construct national balance sheet estimates, it is necessary to have separate land and structure price indexes. However, their hedonic regression model did not estimate separate land and structure price indexes for residential properties. Hill and Scholz (2018) addressed this question and found, using their hedonic regression model, that it was not necessary to use spatial coordinates to obtain satisfactory property price indexes for Sydney. This paper addresses the following question: can satisfactory residential property price indexes be constructed using hedonic regression techniques where location effects are modeled using local neighborhood dummy variables or is it necessary to use spatial coordinates to model location effects. The patterns found are consistent with a market where buyers are either unaware of the risk of flood or unable to evaluate it probabilistically. We use a behavioural framework proposed in earlier literature to construct econometric measurements that provide the opportunity to make statistical inferences on whether the expected behaviours are supported by the data. There are alternative behavioural theories which explain why agents may react in this manner. ![]() It relates property value discounting in response to floods to various theories of market behaviour. ![]() This paper explores patterns of discounting of property prices following infrequent flooding events. Designing protections, adaptations and insurance mechanisms that can equitably ameliorate future impacts requires a detailed understanding of how flood risk affects property prices. The costs of a major event are shared across communities through government funded recovery efforts in the short term, and changes to insurance premiums and house values in the long term. Property owners who purchase properties just before a major flood risk significant personal loss on their largest single asset. The reduction in property values following a major flood event is significant to individuals, communities and governments alike.
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