Macroeconomic Variables Influencing Housing Prices in Vilnius

Laurinavičius, Alfredas; Laurinavičius, Antanas

doi:10.3846/ijspm.2021.15961

Cited by 7 publications

(7 citation statements)

References 34 publications

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“…On the demand side, the selection includes a broad hierarchy of demographic, economic and financial factors at the local, national and international levels. There is evidence that housing demand is driven by population growth dynamics (Borowiecki, 2009;Capozza, et al, 2002;Plakandaras et al, 2015) and migrations (Hanet al, 2018;Laurinavičius, et al, 2021;Taltavull de La Paz & White, 2012). Other demand factors include households' incomes (Abelson, et al, 2005;Mikhed & Zemčík, 2009;Oikarinen, et al, 2018) and employment rate (Engerstam, 2021;Mikhed & Zemčík, 2009;Mohan, et al, 2019;Plakandaras et al, 2015).…”

Section: Literature Reviewmentioning

confidence: 99%

Supply, Demand and Asymmetric Adjustment of House Prices in Poland

Gluszak,

Trojanek

2024

Real Estate Management and Valuation

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In recent years, a lot of empirical effort has been made to search for potential nonlinear responses of house prices to various demand and supply factors. This paper examines Poland's heterogeneous regional housing market reactions to key economic variables from 2000 to 2022. The study raises two research questions related to the asymmetric adjustment of housing markets to selected demand and supply shocks. Firstly, we ask whether the house price adjustments to negative shocks are relatively minor (for example, during an economic downturn) when compared to positive shocks (prevalent in booming markets), mainly due to the loss aversion behavior theory and nominal rigidity of house prices. In particular, we test the hypothesis that house prices react more to positive impulses from supply and demand than negative ones. Secondly, we investigate whether market reactions are similar for large and small metropolitan markets. The second hypothesis posits that metropolitan housing markets adjust asymmetrically to demand and supply shocks based on the city size (population and housing stock in a given city). The study uses a nonlinear ARDL model to test the research hypotheses empirically. This paper provides new insights into the nonlinear reactions of house prices to exogenous shocks and adds to the limited empirical evidence from less developed economies.

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Section: Literature Reviewmentioning

confidence: 99%

Supply, Demand and Asymmetric Adjustment of House Prices in Poland

Gluszak,

Trojanek

2024

Real Estate Management and Valuation

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“…Next, we consider the control variables according to the Laurinavičius et al (2022). The regional economic development level (pgdp) is measured by per capita GDP; the cultural level (culture) is measured by the library number impact about one-third of the local technicians.…”

Section: Variables and Datamentioning

confidence: 99%

The Inter-Relationships Among Mobility, Housing Prices and Innovation: Evidence From China’s Cities

Zhang,

Zhou,

Qian

et al. 2023

International Journal of Strategic Property Management

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The existing literature contends that high housing prices are a negative factor inhibiting population mobility and urban innovation. This paper explores the interaction effect among population mobility, housing prices and urban innovation. Based on panel data from the Yangtze River Delta region from 2010 to 2019, we establish a simultaneous equation model (SEMs) to test the relationship between population mobility, housing prices, and urban innovation vitality. The empirical results show that (1) population mobility and housing prices are positively correlated; (2) urban innovation vitality has a significant and positive effect on housing prices; (3) population mobility and urban innovation vitality are also positively correlated.

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“…For example, labour force and population growth have been used to predict house price dynamics (Borowiecki, 2009;Capozza et al, 2002;Plakandaras et al, 2015). In addition, some paper has addressed more subtle demographic facets, such as the role of migration (Han et al, 2018;Laurinavičius et al, 2022;Taltavull de la Paz & White, 2012).…”

Section: Literature Reviewmentioning

confidence: 99%

Discovering the determinants of house prices dynamics in Poland using Bayesian model averaging

Trojanek,

Gluszak,

Kufel

et al. 2023

Economics &Amp; Sociology

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We examine the price drivers in 18 capital housing markets in Poland from 2004 to 2021. Using the Bayesian Model Averaging method, we discovered that some financial elements explain house price changes more consistently than conventional economic and demographic variables. The dynamics of house prices in most cities were defined explicitly by financial factors – mortgage market activity, mortgage interest rate, and terms and conditions of granting mortgages. Additionally, the stock market played a vital role in house price movement in selected cities. Economic (regional GDP, salaries, unemployment) and demographic (migration) factors were significant predictors of housing price dynamics only for particular cities. Our study closes the knowledge gap on factors influencing home price dynamics in Poland and other developing nations that have gone through systemic change. In the case of Eastern European countries, we have limited knowledge of those linkages.

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Macroeconomic Variables Influencing Housing Prices in Vilnius

Cited by 7 publications

References 34 publications

Supply, Demand and Asymmetric Adjustment of House Prices in Poland

Supply, Demand and Asymmetric Adjustment of House Prices in Poland

The Inter-Relationships Among Mobility, Housing Prices and Innovation: Evidence From China’s Cities

Discovering the determinants of house prices dynamics in Poland using Bayesian model averaging

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