The mismatch between conventional house price modeling and regulated markets: insights from The Netherlands

Tu, Qi; Haan, J. de; Boelhouwer, Peter

doi:10.1007/s10901-016-9529-y

Cited by 12 publications

(9 citation statements)

References 38 publications

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“…The range of regulated markets is broad and heterogeneous, including supply restrictions such as zoning plans and demand-associated arrangements like the tax deduction. Tu, de Haan and Boelhouwer (2016) discussed the different aspects of regulations. 3.…”

Section: Notesmentioning

confidence: 99%

House prices and long-term equilibrium in the regulated market of the Netherlands

Haan

Boelhouwer

2017

Housing Studies

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a otB-Research for the Built environment, delft university of technology, delft, the netherlands; b statistics netherlands, the Hague, the netherlands ABSTRACT This paper establishes a simple affordability model that implicitly incorporates the major Dutch market features to elucidate long-run house prices under a regulatory environment. The results reveal a long-run relationship for house prices under strict regulations. The association among house prices, income, interest rates, and inflation is verified using an aggregated dataset. In the long-run, incomes and interest rates function as the two prime forces driving price dynamics, whereas the role of inflation is limited.

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Section: Notesmentioning

confidence: 99%

House prices and long-term equilibrium in the regulated market of the Netherlands

Haan

Boelhouwer

2017

Housing Studies

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“…When the rents equal the costs of owning, a state of equilibrium is reached. This rent-to-price relationship fits well in markets where rental regulation is absent or limited (Tu, de Haan, & Boelhouwer, 2017). We have based our pricing models on multiple regressions, where the prices of an individual property is a response (dependent) variable, and their associated characteristics, are explanatory (independent) variables.…”

Section: Mathematical Functional Formmentioning

confidence: 68%

An Empirical Testing of a House Pricing Model in the Indian Market

Hoda¹,

JAFRI²,

AHMAD³

et al. 2020

JAFEB

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The main aim of the study is to test a house pricing model by combining hedonic and asset-based pricing models. An understanding of the relationship between house pricing and its return (the rental income) helps to establish houses as a significant asset class. The model tested the relationship between house pricing (dependent variable) and the house attributes (independent variables) derived from Freeman's framework of housing attributes. This study uses a large data-set of 1,899 sample of new, high-end houses purchased between 2016 and 2019 collected from the national capital region of India (Delhi-NCR). The algorithm was built in R-Script, and stepwise multiple linear regression was used to analyze the model. The analysis of the model proves that the three significant variables, namely, carpet area, pay-off, and annual maintenance charges explain the price function. Further, the model is statistically fit. The major contribution of the study is to understand the key factors and their influence on the house pricing. The model will be helpful in risk assessment in the housing investment and enhance the chances of investment. Policy-makers can use information about the underlying valuation drivers of the house prices to stabilize the market and also in framing the tax policies.

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“…In this paper we do not seek to present a new theoretical framework, rather to couch our empirical findings within the conceptual dimensions developed by other studies of how Airbnb might interact with the property market in the broader context of the city. Furthermore, we acknowledge that there are many variables that have a profound impact on housing prices, as well as place-specific complexities derived from different macroeconomic and regulatory environments (Tu et al, 2017). However, we believe it is valuable to study the specific impacts of Airbnb, whose disruption of the housing market is unique and has emerged only over the last decade.…”

Section: Conceptual Frameworkmentioning

confidence: 99%

Spatial Variability of the ‘Airbnb Effect’: A Spatially Explicit Analysis of Airbnb’s Impact on Housing Prices in Sydney

Thackway

Lee

et al. 2022

IJGI

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Over the last decade, the emergence and significant growth of home-sharing platforms, such as Airbnb, has coincided with rising housing unaffordability in many global cities. It is in this context that we look to empirically assess the impact of Airbnb on housing prices in Sydney—one of the least affordable cities in the world. Employing a hedonic property valuation model, our results indicate that Airbnb’s overall effect is positive. A 1% increase in Airbnb density is associated with approximately a 2% increase in property sales price. However, recognizing that Airbnb’s effect is geographically uneven and given the fragmented nature of Sydney’s housing market, we also employ a GWR to account for the spatial variation in Airbnb activity. The findings confirm that Airbnb’s influence on housing prices is varied across the city. Sydney’s northern beaches and parts of western Sydney experience a statistically significant value uplift attributable to Airbnb activity. However, traditional tourist locations focused around Sydney’s CBD and the eastern suburbs experience insignificant or negative property price impacts. The results highlight the need for policymakers to consider local Airbnb and housing market contexts when deciding the appropriate level and design of Airbnb regulation.

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The mismatch between conventional house price modeling and regulated markets: insights from The Netherlands

Cited by 12 publications

References 38 publications

House prices and long-term equilibrium in the regulated market of the Netherlands

House prices and long-term equilibrium in the regulated market of the Netherlands

An Empirical Testing of a House Pricing Model in the Indian Market

Spatial Variability of the ‘Airbnb Effect’: A Spatially Explicit Analysis of Airbnb’s Impact on Housing Prices in Sydney

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