Measuring price dynamics: evidence from the Warsaw housing market

Different valuation methods and determinants of housing prices in Budapest, Hungary are examined in this paper in order to describe price drivers by using an asking price dataset. The hedonic regression analysis and the valuation method of the artificial neural network are utilised and compared using both technical and spatial variables. In our analyses, we conclude that according to our sample from the Budapest real estate market, the Multi-Layer Preceptron (MLP) neural network is a better alternative for market price prediction than hedonic regression in all observed cases. To our knowledge, the estimation of housing price drivers based on a large-scale sample has never been explored before in Budapest or any other city in Hungary in detail; moreover, it is one of the first papers in this topic in the CEE region. The results of this paper lead to promising directions for the development of Hungarian real estate price statistics.

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“…Research on this topic in the Central and Eastern European region is currently scarce (Pavlin 2006;Widlak -Tomczyk 2010).…”

Section: Introductionmentioning

confidence: 99%

Valuation methods for the housing market: Evidence from Budapest

Kutasi

Badics²

2016

Acta Oeconomica

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“…We show that our hedonic index yields a sharper fall in rents after the global financial crisis and more muted developments in the period between 2013 and 2015 than the average rent index. When constructing hedonic indices for the secondary housing market in Warsaw, Widlak and Tomczyk (2010) show similar results. In particular, they find that there is less variability in hedonic indices relative to mean and median indices, which they attribute to hedonic models being better at controlling for compositional changes.…”

Section: Introductionmentioning

confidence: 60%

Location, location, location!^*: a quality-adjusted rent index for the Oslo office market

Anundsen

Bjørland²,

Hagen³

2021

JERER

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PurposeCommonly used rent indices are based on average developments or expert opinions. Such indices often suffer from compositional biases or low data coverage. The purpose of this paper is to overcome these challenges using the authors' approach.Design/methodology/approachThe authors construct a quality-adjusted rent index for the office market in Oslo using detailed data from 14,171 rental contracts.FindingsThe authors show that compositional biases can have a large impact on rental price developments. By adding building-fixed effects to a standard hedonic regression model, the authors show that the explanatory power increases considerably. Furthermore, indices excluding location-specific information, or which include less granular location controls than at the building level, portray quite a different picture of rent developments than indices that do take this into account. The authors also exploit information on contract signature date and find that a more timely detection of turning points can be achieved by using the signature date instead of the more typically used start date of the lease.Research limitations/implicationsThe study is confined to Norwegian data, and an avenue for future research would be to explore if similar results are obtained for other countries. A weakness with the paper is that authors' do not observe quality changes over time, such as renovation. Controlling for time-varying and unit-specific attributes in hedonic models for the commercial real estate (CRE) market would be useful to purge indices further for compositional effects and unobserved heterogeneity. While the authors do control for building-fixed effects, there are additional variations within a building (floor, view, sunlight, etc.) that the authors do not capture. Studies that could control for this would certainly be welcome, both in order to estimate the value of such amenities and to see how it affects estimated rent developments. Another promising avenue for future research is to link data on rental contracts in the CRE market with firm-specific information in order to explore how firm profitability and liquidity may affect rental contracts.Practical implicationsThe authors show that the hedonic index yields a sharper fall in rents after the global financial crisis and more muted developments in the period between 2013 and 2015 than the average rent index. The results show that rents have followed their estimated equilibrium closely and have re-adjusted quickly in periods of deviation. From a financial stability perspective, the risk of a sharp fall in rents is reduced because rents often are in line with their fundamentals.Social implicationsThe authors find that a more timely detection of turning points can be achieved by using information on the signature date. This is an important finding. The financial system is heavily exposed toward CRE, and timely detection of turning points is critical for policymakers.Originality/valueThe financial system is heavily exposed toward the commercial real estate market and timely detection of turning points is of major importance to policymakers. Finally, the authors use our quality-adjusted rent index as the dependent variable in an error correction model. The authors find that employment and stock of offices are important explanatory variables. Moreover, the results show that rents have followed their estimated equilibrium path.

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“…Hill and Trojanek (2020) compare different methods to generate a house price index and find that the hedonic index is a robust method. The hedonic index method was applied in Poland to analyse house prices in Warsaw by Widłak and Tomczyk (2010) and Olszewski et al (2017), and by Trojanek (2009Trojanek ( , 2012Trojanek ( , 2018 to analyse the housing market in Poznań. Given that the hedonic index is a robust measure of house price changes, it should be used for the analysis of the asymmetry of house price cycles.…”

Section: Literature Reviewmentioning

confidence: 99%

The missing asymmetry in the Polish house price cycle: an analysis of the behaviour of house prices in 17 major cities

Brzezicka

Łaszek

Olszewski

et al. 2021

J Hous and the Built Environ

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This paper investigates the house price cycle in 17 major cities in Poland, analysing separately prices of newly constructed housing and prices in the existing stock. We apply the Andre, Gupta and Mwamba (2019) framework and test the deepness and steepness of the cycles. Deepness concerns the relative magnitude of peaks and downturns, while steepness determines, how fast peaks or downturns are reached. When at least one of those measures is not symmetric, there is asymmetry in the cycle. We apply the triples test and the entropy test and find little evidence for asymmetry, mostly only in small cities. This seems to be intriguing, because usually there are asymmetries, for example slow price increases end in sudden drops. The explanation for the smooth cycle in the Polish housing market are a still not satisfied demand for housing and continuously growing wages. Banks issued mortgages only to the more affluent people, which were able to pay back the mortgage. Additionally, two housing subsidy schemes helped to keep house prices stable, when the situation in the global economy worsened, which might have led to price drops.

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Measuring price dynamics: evidence from the Warsaw housing market

Cited by 13 publications

References 16 publications

Valuation methods for the housing market: Evidence from Budapest

Valuation methods for the housing market: Evidence from Budapest

Location, location, location!^*: a quality-adjusted rent index for the Oslo office market

The missing asymmetry in the Polish house price cycle: an analysis of the behaviour of house prices in 17 major cities

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Measuring price dynamics: evidence from the Warsaw housing market

Cited by 13 publications

References 16 publications

Valuation methods for the housing market: Evidence from Budapest

Valuation methods for the housing market: Evidence from Budapest

Location, location, location!*: a quality-adjusted rent index for the Oslo office market

The missing asymmetry in the Polish house price cycle: an analysis of the behaviour of house prices in 17 major cities

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Product

Resources

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Location, location, location!^*: a quality-adjusted rent index for the Oslo office market