What contributes to the rising house prices in Beijing? A decomposition approach

Zhang, Lei; Yi, Yimin

doi:10.1016/j.jhe.2018.04.003

Cited by 17 publications

(16 citation statements)

References 23 publications

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“…There is no consensus among scholars in regard to the selection of percentiles used when conducting QR on housing studies. Among many options (Hodge, 2016;McMillen & Shimizu, 2020), some authors use only the quartiles (Okkola & Brunelle, 2018;Zhang & Yi, 2018) whereas others use the deciles (Liao & Wang, 2012;Zietz et al, 2007). Combining both approaches and using the same criteria of Chen et al (2007), Zahirovich-Herbert and Gibler (2014), and Mora-Garcia et al (2019, the quantile regression was carried out for three quartiles (the 25th, the 50th, and the 75th quantile) as well as for the first decile (the 10th quantile which represent the bottom segment) and the last decile (the 90th quantile which represent the top segment).…”

Section: Methodsmentioning

confidence: 99%

How on Earth Did Spanish Banking Sell the Housing Stock?

Torres-Pruñonosa

García-Estévez

Raya³

et al. 2022

SAGE Open

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The accumulation of properties by Spanish banks during the crisis of the first decade of the 21st century has definitely changed the housing market. An optimal house price valuation is useful to determine the bank’s actual financial situation. Furthermore, properties valued according to the market can be sold in a shorter span of time and at a better price. Using a sample of 24,781 properties and a simulation exercise, we aim to identify the decision criteria that Spanish banking used to decide which properties were going to be sold and at what price. The results of the comparison among four methods used to value real estate—artificial neural networks, semi log regressions, a combined model by means of weighted least squares regression, and quantile regressions—and the actual situation suggest that banking aimed to maximize the reversal of impairment losses, although this would mean capital losses, selling less properties, and decreasing their revenues. Therefore, the actual combined result was very detrimental to banking and, consequently, to the Spanish society because of its banking bailout.

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Section: Methodsmentioning

confidence: 99%

How on Earth Did Spanish Banking Sell the Housing Stock?

Torres-Pruñonosa

García-Estévez

Raya³

et al. 2022

SAGE Open

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“…This pricing method is based on the real estate characteristics, where the price is calculated by taking into account the impact of different housing price characteristics. Real estate prices are affected by many different characteristics, such as supply and demand in the real estate market, the surrounding environment and other factors (Bahmani-Oskooee & Zhang & Yi, 2018;Wu, Sah, & Tidwell, 2018). These features are used to establish a feature function graph and sensitivity analysis of different influential factors is carried out.…”

Section: Real Estate Appraisal and Financial Engineering Theoriesmentioning

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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“…When the estimation of the conditional mean cannot capture the links between the explanatory variables and the dependent variable throughout the whole distribution of the latter, QRs are frequently used. QRs have also recently been used in the literature on housing economics [23][24][25][26][27][28][29][30][31][32][33][34][35][36].…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network, Quantile and Semi-Log Regression Modelling of Mass Appraisal in Housing

2021

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We used a large sample of 188,652 properties, which represented 4.88% of the total housing stock in Catalonia from 1994 to 2013, to make a comparison between different real estate valuation methods based on artificial neural networks (ANNs), quantile regressions (QRs) and semi-log regressions (SLRs). A literature gap in regard to the comparison between ANN and QR modelling of hedonic prices in housing was identified, with this article being the first paper to include this comparison. Therefore, this study aimed to answer (1) whether QR valuation modelling of hedonic prices in the housing market is an alternative to ANNs, (2) whether it is confirmed that ANNs produce better results than SLRs when assessing housing in Catalonia, and (3) which of the three mass appraisal models should be used by Spanish banks to assess real estate. The results suggested that the ANNs and SLRs obtained similar and better performances than the QRs and that the SLRs performed better when the datasets were smaller. Therefore, (1) QRs were not found to be an alternative to ANNs, (2) it could not be confirmed whether ANNs performed better than SLRs when assessing properties in Catalonia and (3) whereas small and medium banks should use SLRs, large banks should use either SLRs or ANNs in real estate mass appraisal.

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What contributes to the rising house prices in Beijing? A decomposition approach

Cited by 17 publications

References 23 publications

How on Earth Did Spanish Banking Sell the Housing Stock?

How on Earth Did Spanish Banking Sell the Housing Stock?

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

Artificial Neural Network, Quantile and Semi-Log Regression Modelling of Mass Appraisal in Housing

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