Exploring Spatial Dynamics with Land Price Indexes

Abstract

The purpose of this study is to explore the within-region spatial dynamics of appreciation and depreciation rates using three different representations of geographical space. Mean value indexing methods are used to construct global land price indexes, sub-market land price indexes and local land price indexes using transaction price data for vacant residential land within the City of Hamilton, Ontario, between 1995 and 2003. The results are validated against Statistics Canada’s series of New Housing Price Indexes, followed by a comparison of the relative performance of the three geographical representations of land price indexes. The results indicate that the mean value indexing methods are robust, although subject to outliers and sample selection bias, and clearly illustrate the spatial dynamics of annual appreciation and depreciation rates across the study area. The results also underscore the need for regular surveillance of the spatial dynamics of urban land markets.

Factors affecting farmland prices in the Czech Republic

“…We therefore conclude that the main reason behind this sharp increase in farmland prices is speculative interest motivated by conversion of farmland and subsequent sale of the land for non-farm uses. This is in accordance with findings of Drozd and Johnson (2004), Tan et al (2009) andSpinney et al (2011). In the Czech Republic, buildable land is usually delimited by the Master plan, and development permits are nearly always granted only in direct continuity with the current built-up area.…”

“…Higher soil fertility and its influence on land prices mainly reflect interest in using the land for agriculture, as the prices of building plots, recreational plots and other plots are usually not determined by soil fertility (Sklenicka et al, 2002). The only exception may be the least fertile farmland, where speculative purchases for non-farming purposes are supported by the ease and the higher probability of obtaining permission to convert the land to residential use, as well as by the significantly lower financial cost of obtaining this permission than in the case of more fertile land (Forster, 2006;Spinney et al, 2011). This phenomenon is manifested in our results, where higher farmland prices were observed both in the most fertile land and in the least fertile land.…”

“…Barnard (2000) estimates that non-agricultural influences account for about onequarter of the average market value of US farm real estate. In such cases, location-specific characteristics, which do not reflect the agricultural characteristics of the land, are capitalized into land prices (Cho and Newman, 2005;Tan et al, 2009;Spinney et al, 2011).…”

Property value map updating by mass appraisal method – a case in the city of Pato branco, state Paraná

“…Said studies, as presented below, used a number of methods and different types of variables in order to find the best appraisal results for the properties. Spinney et al (2011) used average value indexing methods to produce values for lots transacted in the city of Hamilton, located in the province of Ontario, Canada. In China, in 21 provincial cities, Wen and Goodman (2013), using an approach with modeling of simultaneous equations and the two-stage least squares method, analyzed the correlation between land price and habitation price.…”

Predictive land value modelling in Guatemala City using a geostatistical approach and Space Syntax

“…Morales et al (2017b) reported that including SSx metrics and additional submarket information, as suggested by Bourassa et al (2007), leads to a reduction of spatial dependence but does not completely overcome it. A typical strategy to deal with autocorrelated errors in a multivariate regression (MR) is to consider more predictors, submarket variables or even the observation coordinates prior to adopting spatial modelling techniques (Des Rosiers et al 2000, Bourassa et al 2010, Seya et al 2011, Spinney et al 2011. For inferential purposes this might be beneficial at the expense of more complex models and overfitting.…”