Purpose: Commercial real estate (CRE) indicators typically include asset deals and exclude share deals. This study aims to explore the phenomenon of real estate share deals and assess whether omitting these transactions results in indicators that do not accurately reflect the market. Design/methodology/approach: Various registers in the Netherlands were used to estimate transaction volumes, total values and price developments of both share and asset deals. Share deals are company transfers and its transactions cover more than real estate. To estimate the contribution of real estate in share deals, valuations were used. Findings: In the Netherlands, share deals are most prominent for rental dwellings. Adding share deals to volume and value indicators seems required. In price development estimates, significant differences were found for dwellings between share and asset deals. Price indices should, therefore, also include share deals, but in practice this is difficult and has little impact on the outcomes due to the low weight of share deals. Research limitations/implications: Legislation has a major impact on choosing a share or asset deal. The significance of share deals is expected to vary amongst countries. Performing similar research in other countries will contribute in harmonising real estate indicators. Practical implications: Statistical agencies face many challenges in the construction of CRE indicators. This study provides statisticians knowledge that can be used to evaluate possible data gaps. Originality/value: This is the first study to estimate indicators of real estate share deals and compare these to asset deal indicators.

Over the last decade, house prices have increased substantially in nearly all OECD countries. These house price increases frequently coincided with changes in mortgage credit conditions; i.e., decreases in the interest rate and increases in income. This is in line with existing literature, which finds an equilibrium relationship between mortgage credit and house prices. The literature, however, lacks an analysis of what drives the equilibrium, which we assess in this paper. Moreover, we propose a combination of two explanations discussed in the literature. That is, we argue that lower-income households are bound by credit constraints, while higher-income households have a preference for spending a fixed fraction of income on mortgage payments. We develop theoretical models for all three explanations and test the models using data on the Dutch property market. The empirical results clearly support the combined approach. Overall, the results suggest that it is important to differentiate between types of households when forecasting house prices or assessing the effectiveness of policy interventions.

This paper compares two model-based multilateral price indexes: the time-product dummy (TPD) index and the time dummy hedonic (TDH) index, both estimated by expenditure-share weighted least squares regression. The TPD model can be viewed as the saturated version of the underlying TDH model, and we argue that the regression residuals are “distorted toward zero” due to overfitting. We decompose the ratio of the two indexes in terms of average regression residuals of the new and disappearing items. The decomposition aims to explain the conditions under which the TPD index suffers from quality-change bias or, more generally, lack-of-matching bias. An example using scanner data on packaged men's T-shirts illustrates our framework.

Cross-city spillovers among housing markets are usually modelled by the classical spatial autoregressive models, which usually suffer from identification problems in practice. This paper investigates the cross-city house price spillovers arising from city network externalities wherein a city's connections with other cities in the urban network create the external house price premium through productivity and amenity gains. Using a cross-sectional data set for an urban system in eastern China, we present significant evidence for positive network spillovers by the application of spatial lag of X model and spatial Durbin error model. Besides, common shocks are also proved to be responsible for cross-city dependence of house prices.

Location is capitalized into the price of the land the structure of a property is built on, and land prices can be expected to vary significantly across space. We account for spatial variation of land prices in hedonic house price models using geospatial data and a semi-parametric method known as mixed geographically weighted regression. To measure the impact on aggregate price change, quality-adjusted (hedonic imputation) house price indices are constructed for a small city in the Netherlands and compared to price indices based on more restrictive models, using postcode dummy variables, or no location information at all. We find that, while taking spatial variation of land prices into account improves the model performance, the Fisher house price indices based on the different hedonic models are almost identical. The land and structures price indices, on the other hand, are sensitive to the treatment of location.

Purpose: This paper aims to examine the existence of the ripple effect from Amsterdam to the housing markets of other regions in The Netherlands. It identifies which regional housing markets are influenced by house price movements in Amsterdam. Design/methodology/approach: The paper considers the ripple effect as a lead-lag effect and a long-run convergence between the Amsterdam and regional house prices. Using the real house prices for second-hand owner-occupied dwellings from 1995q1 to 2016q2, the paper adopts the Toda–Yamamoto Granger Causality approach to study the lead-lag effects. It uses the autoregressive distributed lags (ARDL)-Bounds cointegration techniques to examine the long-run convergence between the regional and the Amsterdam house prices. The paper controls for house price fundamentals to eliminate possible confounding effects of common shocks. Findings: The cumulative evidence suggests that Amsterdam house prices have influence on (or ripple to) all the Dutch regions, except one. In particular, the Granger Causality test concludes that a lead-lag effect of house prices exists from Amsterdam to all the regions, apart from Zeeland. The cointegration test shows evidence of a long-convergence between Amsterdam house prices and six regions: Friesland, Groningen, Limburg, Overijssel, Utrecht and Zuid-Holland. Research limitations/implications: The paper adopts an econometric approach to examine the Amsterdam ripple effect. More sophisticated economic models that consider the asymmetric properties of house prices and the patterns of interregional socio-economic activities into the modelling approach are recommended for further investigation. Originality/value: This paper focuses on The Netherlands for which the ripple effect has not yet been researched to the authors’ knowledge. Given the substantial wealth effects associated with house price changes that may shape economic activity through consumption, evidence for ripples may be helpful to policy makers for uncovering trends that have implications for the entire economy. Moreover, the analysis controls for common house price fundamentals which most previous papers ignored.

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.

This paper uses individual house transaction data from 1995 to 2014 in Amsterdam to explore the risks and interrelationships of the subdistrict house prices. Simple indicators suggest that house prices grow faster and are more risky in the central business district and its immediate surrounding areas than in the peripherals. Furthermore, we observe an over time decreasing intervariations between the subdistrict house price growth rates, whereas we find a lead–lag and house price causal flow from the more central to the peripheral subdistricts.