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Housing property along riverbanks in Taipei, Taiwan: a spatial quantile modelling of landscape benefits and flooding losses

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Abstract

The purpose of this study is to compare the riverbank landscape benefit and flooding loss for housing property types with different price levels along the Tamsui River in Taipei, Taiwan. The most important contribution of this study is the empirical accomplishment of analysing a large dataset of 23,948 transacted housing properties by using a spatial lag quantile regression model. The overall results show that the riverbank landscape benefit on both sides of the Tamsui River is larger than the flooding loss. By housing type, the flooding losses for some types of housing properties with specific price levels are larger than the riverbank landscape benefits. However, for housing properties with the highest prices, the riverbank landscape benefit is in general larger than its corresponding flooding loss. Accounting for both the riverbank landscape benefit and flooding loss for every type of housing property provides comprehensive guidance and gives urban development a constructive direction.

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Source: Rearranged from Lee and Li (2009)

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Notes

  1. One more distinguishing characteristic of quantile regression model is robust to outliers in small sample size (Fingleton and Le Gallo 2008). However, the sample size used in this study is large enough for not revealing such advantage.

  2. Thanks to a reviewer who pointed out the necessity of applying bootstrapping to the quantile model. However, bootstrapping is unsuitable in the present study because the sample size is already large, with 23,948 housing units. This footnote is intended as supplementary information for readers on the linkage between bootstrapping and quantile models. When a richer characterization of the entire dataset, not merely the mean, is the purpose of the analyses, a quantile model is employed because under such circumstances OLS is not efficient. It is further known from Nikitina et al. (2019) that if the sample size is small then the bootstrapping method can be performed to replicate and enlarge the sample size. Thus, a bootstrapping quantile model can deal with the difficulties for studies with small samples. However, a bootstrapping quantile model is not robust in some case, because bootstrapping might have a tendency to proliferate outliers in the sample size simulation.

  3. Among these 23,948 transacted housing units only 217 are repeated sells. The repeated sells of housing make up less than 1% of the sample size used in the final analyses. Additionally, all the housing prices are deflected by the consumer price index of 2014 to place them within the same timeline; thus, the sample is neither a pooling nor a panel type of data.

  4. Other variables influence the prices of individual housing properties. These variables include the crime rate, school quality, air pollution, and drinking water quality. However, these variables are highly correlated with the district. If these variables are used as independent variables in the estimation, the estimation results will have an unusually large number of standard errors. As a result, we have to choose either district variables or all of the other explanatory variables that are mentioned above to avoid possible multicollinearity among them and to maintain the significance for all the district variables that remain in the estimated equations.

  5. The idea proposed by Kim and Muller (2004) and that of Chernozhukov and Hansen (2006) is originally to solve the problem of quantile regression. Kim and Muller (2004) develop a double-stage quantile regression and Chernozhukov and Hansen (2006) suggest an instrument variable to correct the potential heterogeneous and non-constant effect of independent variable on dependent variable across quantiles. The first stage of both approaches is accomplished by creating new variables. In broad sense, vector of spatial lag pricings are variables generated in spatial lag model in the first stage. Since these variables with high probability give rise to endogeneity with other existing variables, the second stage of both approaches then devote different efforts to resolve such issue.

  6. The case we have here is that change in the distance to riverbank and depth of flood both are measured in metre. But their meanings in fact are different. Distance between housing and the riverbank means how closer or how far away between these two objects. The other metre is a measurement for the depth of the flooding water. Most frequently, in many cases the marginal benefit or loss for one variable could be measured in metre for instance and the marginal effect for the other variable might be measured in kilograms. The multiplication of the marginal impact, treated as a unit price in hedonic price underlying theory, and the amount of the variable will obtain the total impact.

  7. Some ‘office buildings’ in Taipei also contain a limited number of residential units.

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Authors and Affiliations

  1. Department of Agricultural Economics, National Taiwan University, No. 1, Section 4, Roosevelt Road, Taipei, Taiwan

    Pei-Ing Wu

  2. Arboriculture and Landscape Management Office, Yilan, Taiwan

    Yi Chen

  3. The Center for Green Economy, Chung-Hua Institution for Economic Research, Taipei, Taiwan

    Je-Liang Liou

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  1. Pei-Ing Wu
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Wu, PI., Chen, Y. & Liou, JL. Housing property along riverbanks in Taipei, Taiwan: a spatial quantile modelling of landscape benefits and flooding losses. Environ Dev Sustain 23, 2404–2438 (2021). https://doi.org/10.1007/s10668-020-00680-7

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