House price dynamics in Iberian Metropolitan Statistical Areas: slope heterogeneity, cross-sectional dependence and elasticities

Cunha, António M.; Lobão, Júlio

doi:10.1108/jerer-02-2022-0005

Cited by 3 publications

(5 citation statements)

References 47 publications

(123 reference statements)

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“…This paper is close to Oikarinen et al (2018) and Cunha and Lobão (2022) in objectives and partly in methodology. All the studies are multicity multivariate panel spatial analyses.…”

Section: House Price Dynamicsmentioning

confidence: 77%

“…Corresponding estimates for construction costs is 0.07. The income elasticities of demand reported by other studies is as follows: Holly et al (2010) in the USA (1.14), Matysiak and Olszewski (2019) in Warsaw market as (−0.894), Cunha and Lobão (2022) estimate the long-run coefficient as (0.227) in Iberian cities. The mean coefficient for the 14 cities with positive parameters is 0.36, and the remaining 21 cities with negative sign for, the coefficient is 0.41.…”

Section: Empirical Analysismentioning

confidence: 97%

“…The authors use a bounds test to test the long-term cointegration among house price indexes and housing rent, construction cost and real mortgage interest rate. Cunha and Lobão (2022) study the dynamics and elasticities of house prices in Spain and Portugal. In these markets the house prices are inelastic to income.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Mean group estimates that provide robust estimates given these challenges are preferred. These include the fully modified ordinary least square augmented least squares (FMOLS-AMG) deployed in Oikarinen et al (2018) and Cunha and Lobão (2022). The later study deploys the DCCEMG for short-run estimation.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Estimation in Oikarinen et al (2018) is based on fully modified OLS-augmented mean groups (FMOLS-AMG) estimator. Cunha and Lobão (2022) estimation is based both on FMOLS-AMG and dynamic common correlated effects mean groups (DCCEMG). The CCEMG estimator is found satisfactory for empirical analysis in this paper.…”

Section: Introductionmentioning

confidence: 99%

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House price dynamics under lower leverage: the case of metropolitan cities in India

Pani

2022

IJHMA

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Purpose This paper aims to examine the dynamics of house prices in metropolitan cities in an emerging economy. The purpose of this study is to characterise the house price dynamics and the spatial heterogeneity in the dynamics. Design/methodology/approach The author explores spatial heterogeneity in house price dynamics, using data for 35 Indian cities with a million-plus population. The research methodology uses panel econometrics allowing for spatial heterogeneity, cross-sectional dependence and non-stationary data. The author tests for spatial differences and analyses the income elasticity of prices, the role of construction costs and lending to the real estate industry by commercial banks. Findings Long-term fundamentals drive the Indian housing markets, where wealth parameters are stronger than supply-side parameters such as construction costs or availability of financing for housing projects. The long-term elasticity of house prices to aggregate household deposits (wealth proxy) varies considerably across cities. However, the elasticity estimated at 0.39 is low. The highest coefficient is for Ludhiana (1.14), followed by Bhubaneswar (0.78). The short-term dynamics are robust and show spatial heterogeneity. Short-term momentum (lagged housing price changes) has a parameter value of 0.307. The momentum factor is the crucial dynamic in the short term. The second driver, the reversion rate to long-term equilibrium (estimated at −0.18), is higher than rates reported from developed markets. Research limitations/implications This research applies to markets that require some home equity contributions from buyers of housing services. Practical implications Stakeholders can characterise stable housing markets based on long-term fundamental value and short-run house price dynamics. Because stable housing markets benefit all stakeholders, weak or non-existent mean reversion dynamics may prompt the intervention of policymakers. The role of urban planners, and local and regional governance, is essential to remove the bottlenecks from the demand side or supply side factors that can lead to runaway prices. Originality/value Existing literature is concerned about the risk of a housing bubble due to relaxed credit norms. To prevent housing market bubbles, some regulators require higher contributions from home buyers in the form of equity. The dynamics of house prices in markets with higher owner equity requirements vary from high-leverage markets. The influence of wealth effects is examined using novel data sets. This research, documents in an emerging market context, the observations cited in low-leverage developed markets such as Germany and Japan.

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“…This paper is close to Oikarinen et al (2018) and Cunha and Lobão (2022) in objectives and partly in methodology. All the studies are multicity multivariate panel spatial analyses.…”

Section: House Price Dynamicsmentioning

confidence: 77%

Section: Empirical Analysismentioning

confidence: 97%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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House price dynamics under lower leverage: the case of metropolitan cities in India

Pani

2022

IJHMA

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Do real estate investment companies profit from house price growth? Evidence from Portugal

Cunha

Borges²,

Ferreira³

2023

IJHMA

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Purpose This study aims to study the sensitivity of nonlisted real estate investment companies’ accounting earnings to house prices. This study evaluates whether house price changes determined these companies’ return on equity (ROE) or if other factors influenced the industry’s profitability beyond house price growth. Design/methodology/approach The authors collected a ten-year sample with the aggregate ROE of Portugal’s real estate investment companies, split by regions, and data on house prices and the per capita gross domestic product as a control variable. The authors ran a national-level time series with the canonical cointegrating regression estimator, which is robust to a small sample size; the authors also performed a regression on regional-level panel data with the common correlated effects mean group estimator, thus allowing slope coefficient heterogeneity and controlling for cross-sectional dependence. The authors also ran ordinary least squares regressions as a means of comparison. Findings This study found that an increase in the house price is not translated into an increase in the aggregate ROE. The results are robust with a reduced survivorship-biased sample, meaning that even the best-succeeded real estate investment companies do not have their accounting ROE dependent on house price growth. Research limitations/implications The sample size is small and specific to one country. This paper did not study the housing market structure to verify whether it operates under monopolistic competition, which could further explain the attained results. Practical implications Policy decision-makers should know that there are no excess profits in the real estate investment companies’ industry because of house price growth that could be subject to windfall taxes. Originality/value To the best of the authors’ knowledge, the connections between house prices and real estate investment companies’ accounting earnings have never been studied.

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Housing price dynamics and elasticities: Portugal's conundrum

Cunha,

Loureiro

2024

NAR

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<abstract> We aimed to estimate the housing price determinants and elasticities in Portugal's metropolitan areas to help understand the dynamics of the abnormal price increase of the last decade, one of the highest in Europe and the World. We followed a three-step methodology applying panel data and time series regression estimation. First, we estimated the determinants of housing prices at the national and metropolitan area levels. Second, we split the sample by coastal and inner metropolitan areas and estimated the determinants of housing prices and the supply elasticities of each group. Third, we estimated the correlations between housing price growth and elasticities to find whether these determinants correlate. The results showed that at the national level, housing prices are inelastic to aggregate income (0.112). <italic>Momentum</italic> is the most significant determinant of housing prices (0.760). At the metropolitan areas level, we found an inelastic housing supply, a price-to-income elasticity close to zero, and a more inelastic supply in coastal areas. We found no significant correlation between housing price growth, price-to-income, and supply elasticity. The coastal areas registered housing price growth and a <italic>momentum</italic> effect much higher than the inner areas, suggesting the existence of dynamic speculative forces that cause prices to move beyond what can be explained by equilibrium models. The present study contributes to the literature on housing price dynamics by showing that the conventional equilibrium stock-flow model does not explain the increase in Portugal's current housing prices, suggesting that other forces (such as economic uncertainty and sentiment) determine the housing price dynamics. The explanation for the housing price growth in Portugal is a <italic>conundrum</italic>. We believe this knowledge can help define better housing policies at the local and national levels. </abstract>

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House price dynamics in Iberian Metropolitan Statistical Areas: slope heterogeneity, cross-sectional dependence and elasticities

Cited by 3 publications

References 47 publications

House price dynamics under lower leverage: the case of metropolitan cities in India

House price dynamics under lower leverage: the case of metropolitan cities in India

Do real estate investment companies profit from house price growth? Evidence from Portugal

Housing price dynamics and elasticities: Portugal's conundrum

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