Effects of the housing price to income ratio on tenure choice in Taiwan: forecasting performance of the hierarchical generalized linear model and traditional binary logistic regression model

Lee, Chun-Chang; Liang, Chih-Min; Chen, Jian-Zheng; Tung, Cheng-Huang

doi:10.1007/s10901-017-9572-3

Cited by 8 publications

(7 citation statements)

References 30 publications

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“…Evidence of unaffordable housing in lower income groups and in urban areas may also help to explain declining lower income groups ownership ratio in Turkey (Cos kun, 2016b). This evidence is in line with the empirical literature focusing on similar affordability crises such as in China (Kuang and Li, 2012;Wang et al, 2012;Yang and Chen, 2014;Shi et al, 2016) and in Taiwan (Lee et al, 2018).…”

Section: Ijhma 143supporting

confidence: 90%

“…However, in the aftermath of the Global Financial Crisis, housing affordability has been on a steady decline. Lee et al (2018) find that housing price to income ratio had a significant negative effect on tenure choice; a higher ratio of housing price to income resulted in a higher preference among households towards leasing in Taiwan. By applying price-to-income ratio approach and the 30% rule, Uwayezu and Vries (2020) find that less than 30% of households can afford a house from the housing packages proposed by the real estate developers in Kigali, Rwanda.…”

Section: Measuring Homeownership Affordabilitymentioning

confidence: 86%

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Measuring homeownership affordability in emergent market context: an exploratory analysis for Turkey

Coşkun

2020

IJHMA

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Purpose This paper aims to offer an extensive empirical case study analysis by investigating housing affordability in Turkey as a whole, and in Istanbul, Ankara and Izmir over the period of 2006 and 2017 and its sub-periods. Design/methodology/approach This paper develops a theoretically informed model to assess affordability using complementary methodologies in quantitative analysis. This study seeks to help outline the nature of the problem in aggregate level and in the cities; it also seeks to offer lessons about how to address measurement and modelling challenges in emergent market contexts by constructing aggregate-/city-level housing cost-to-income (HCI) ratio, adjusted HCI (AHCI) ratio, housing affordability index (HAI) and effective HAI sensitive to multiple calculation methodologies and alternative data set involving income distribution and poverty tranches. Findings HCI, AHCI, HAI and EHAI models generally suggest the parallel results: housing is not affordable in Turkey and in Istanbul, Ankara and Izmir except for the highest income groups. The evidence implies that besides macroeconomic instabilities, distorted interest rates and short average mortgage maturity, poverty and unequal income/wealth distributions are the main reasons of the Turkish housing affordability crisis specifically heightened in metropolitan areas such as in Istanbul. Research limitations/implications The evidence provides an insight on housing affordability problems in Turkey. However, small sample size and short observation period create a limit for generalisation of the findings. Further analysis would be required to illustrate how housing affordability changes in different cities of Turkey in a longer period. Practical implications By using empirical approaches, this paper helps to understand how serious housing affordability problems of Turkey in aggregate and urban levels. This evidence helps to explain declining ownership ratio in low-income groups and in urban areas. Reliable explanations on existing housing crisis of Turkey also help to develop affordable housing policies. Social implications Declining housing affordability and homeownership ratio may translate as the rising housing inequality and insecurity among Turkish households. Moreover, better affordability values of higher income groups suggest that existing inequality, economic/social segmentation, and hence social tension between high and low income groups, may further increase. In this respect, the authors suggest socially important policies such as reducing income/wealth inequalities and increasing affordable housing supply. Originality/value This study offers a detailed empirical case study analysis that can be used as an exemplar of how to overcome data constraints in other evolving housing market contexts. This study sets out an approach overcoming the challenges of measurement. This study also combines existing methodological approaches with the modified variables to provide a more realistic aggregate-/urban-level housing affordability picture. The authors calculated some parts of housing affordability ratio and index series using discretionary income, minimum wage and effective minimum wage to show the variations of different measurement approaches. Some constructed series are also sensitive to income distribution and poverty thresholds. Collectively, this empirical approach, developed by using emerging market data, provides a contribution to the literature.

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Section: Ijhma 143supporting

confidence: 90%

Section: Measuring Homeownership Affordabilitymentioning

confidence: 86%

Measuring homeownership affordability in emergent market context: an exploratory analysis for Turkey

Coşkun

2020

IJHMA

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“…(1) whether the characteristic value is regarded as either constant or log-linear (Li & Brown, 1980;Des Rosiers, Lagana, & Theriault, 2001;Kopczewska & Lewandowska, 2018;Jonkman, Janssen-Jansen, & Schilder, 2018;Mastromonaco & Maniloff, 2018;J. J. Zhang, Fu, Chen, Chu, & C. C. Zhang, 2018;Lee, Liang, Chen, & Tung, 2018). For example, the price and the distance from transportation are often regarded as linear or log-linear, but some transportation hubs such as busy MRT stations, or airports, can also be sources of noise pollution which could have a negative impact on housing prices.…”

Section: Real Estate Appraisal and Financial Engineering Theoriesmentioning

confidence: 99%

“…MRT stations in metro area), real estate appraisal becomes incorrect thus rendering it impossible to analyse. Furthermore, although there are some methods discussing the spatial problem while using the hedonic concept, those methods cannot express or quantify the ripple effect by multi-attributes well (Zhang et al, 2019;Hill & Scholz, 2018;Lee et al, 2018;Wen, Chu, Zhang, & Xiao, 2018;. A dynamic mathematical function that can explain nonlinear impact by surrounding multi-attributes, measure the ripple effect, and provide relatively accurate assessment is important against the existing methods.…”

Section: Introductionmentioning

confidence: 99%

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

Chen

Azzizi

et al. 2020

International Journal of Strategic Property Management

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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 majority of the studies on tenure choices deal with the explanatory variables that influence the tenure choices of households. These variables include the characteristics of the heads of households, property attributes, funding sources for property purchases, social statuses, neighborhood factors, and policy effects (Lee et al, , 2018. In fact, most of these studies conduct analyses with logistic regression models.…”

Section: Literature Reviewmentioning

confidence: 99%

A Comparison of the Predictive Powers of Tenure Choices Between Property Ownership and Renting

Lee

Liang

Liu

2019

International Journal of Strategic Property Management

Self Cite

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This paper compares the predictive powers of hierarchical generalized linear modeling (HGLM), logistic regression, and discriminant analysis with regard to tenure choices between buying property and renting property by sampling the residents of the Greater Taipei area. The results imply that the hit rate and other indicators included in HGLM have better predictive power with regard to tenure choices than the binary logistic regression model and the discriminant analysis model. That is, using HGLM to process nested data can increase prediction accuracy regarding household tenure choices. Furthermore, cross-validation is performed to analyze hit rate stability. The hit rate sequencing from this cross-validation is found to be consistent with the HGLM results, implying that the comparison of the three models in terms of hit rate performance prediction in this study is stable and reliable.

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Effects of the housing price to income ratio on tenure choice in Taiwan: forecasting performance of the hierarchical generalized linear model and traditional binary logistic regression model

Cited by 8 publications

References 30 publications

Measuring homeownership affordability in emergent market context: an exploratory analysis for Turkey

Measuring homeownership affordability in emergent market context: an exploratory analysis for Turkey

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

A Comparison of the Predictive Powers of Tenure Choices Between Property Ownership and Renting

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