Marc K. Francke

The Hierarchical Trend Model for property valuation and local price indices

This paper presents a hierarchical trend model (HTM) for selling prices of houses, addressing three main problems: the spatial and temporal dependence of selling prices and the dependency of price index changes on housing quality. In this model the general price trend, cluster-level price trends, and specific characteristics play a role. Every cluster, a combination of district and house type, has its own price development. The HTM is used for property valuation and for determining local price indices. Two applications are provided, one for the Breda region, and one for the Amsterdam region, lying respectively south and north in The Netherlands. For houses in these regions the accuracy of the valuation results are presented together with the price index results. Price indices based on the HTM are compared to a standard hedonic index and an index based on weighted median selling prices published by national brokerage organization. It is shown that, especially for small housing market segments the HTM produces price indices which are more accurate, detailed, and up-to-date.

Efficient Computation of Hierarchical Trends

To model a large database containing selling prices for houses, in which local trends, general trends, and specific characteristics play a role, we derived a new procedure to implement a state-space model for repeated measurements. The original model is decomposed into two parts, which are treated differently. The first part is ordinary least squares on data in deviation from means. This step provides a prior for coefficients to be used in the second step, which is a Kalman filter, providing estimates of the trends and the parameters. The procedure exploits and illustrates the Bayesian interpretation of a Kalman filter.

Likelihood Functions for State Space Models with Diffuse Initial Conditions

State space models with non-stationary processes and/or fixed regression effects require a state vector with diffuse initial conditions. Different likelihood functions can be adopted for the estimation of parameters in time-series models with diffuse initial conditions. In this article, we consider profile, diffuse and marginal likelihood functions. The marginal likelihood function is defined as the likelihood function of a transformation of the data vector. The transformation is not unique. The diffuse likelihood is a marginal likelihood for a data transformation that may depend on parameters. Therefore, the diffuse likelihood cannot be used generally for parameter estimation. The marginal likelihood function is based on an orthonormal data transformation that does not depend on parameters. Here we develop a marginal likelihood function for state space models that can be evaluated by the Kalman filter. The so-called diffuse Kalman filter is designed for computing the diffuse likelihood function. We show that a minor modification of the diffuse Kalman filter is needed for the evaluation of our marginal likelihood function. Diffuse and marginal likelihood functions have better small sample properties compared with the profile likelihood function for the estimation of parameters in linear time series models. The results in our article confirm the earlier findings and show that the diffuse likelihood function is not appropriate for a range of state space model specifications.

Land, structure and depreciation

We introduce a hedonic price model that enables us to disentangle the value of a property into the value of land and the value of structure. For given reconstruction costs, we are able to estimate the impact of physical deterioration, functional obsolescence and vintage effects on the structure and the impact of time on sale (and external obsolescence) on the land value simultaneously. Our findings show that maintenance has a substantial impact on the rate of physical deterioration. After 50 years of not or barely maintaining a home, a typical structure has lost around 43% of its value. In contrast, maintaining a home very well results in virtually no physical deterioration in the long run.

The Effect of Credit Conditions on the Dutch Housing Market

It is widely perceived that the supply of mortgages, especially since the extensive liberalization of the mortgage market of the 1980s, has had implications for the housing market in the Netherlands. In this paper we introduce a new method to estimate a credit condition index (CCI). The CCI represents changes in the supply of credit over time, apart from changes in interest rates and income. It has been estimated by an unobserved component in an error-correction model in which the average amount of new provided mortgages per period is explained by the borrowing capacity and additional control variables. The model has been estimated on data representing first time buyers (FTB). For FTB we can assume that the housing and non-housing wealth is essentially zero. The CCI has subsequently been used as an explanatory variable in an error-correction model for house prices representing not only FTB, but all households. The models have been estimated on quarterly data from 1995 to 2012. The estimated CCI has a high correlation with the Bank Lending Survey, a quarterly survey in which banks are asked whether there is a tightening or relaxation of (mortgage) lending standards compared to the preceding period. The CCI has explanatory power in the error-correction model for house prices. In real terms house prices declined about 25% from 2009 to 2012. The estimation results show that nearly half of this decline can be attributed to a decline in the CCI.

Evaluation of House Price Models Using an ECM Approach: The Case of the Netherlands

The research question of this paper is whether the Dutch housing market is overvalued or not. This is investigated by using different types of error correction models and by examining the impact of different variables that can explain house price changes in the Netherlands. The current financial crisis confirms the notion that developments in the residential property sector are important for the economy as a whole. For that reason it is important to fully understand the factors that affect the housing market. Therefore we need a long-run model approach that relates house prices to fundamentals. However the model should also be able to detect bubbles in the short run. As a first step, we look at the affordability of house prices and mortgage payments in order to check how well the housing market performs in the short run. In the medium to long-run, we estimate an error correction model relating prices to fundamentals, using variables like interest rate, labour income, financial assets of households, and household stock. The error correction model tests whether prices tend to revert to some equilibrium price level. We evaluate existing house price models for the Netherlands, which we use as a benchmark for comparison to our improved model. Finally, we try to forecast housing prices based on a few simple economic scenarios.

Internet search behavior, liquidity and prices in the housing market

In this article we employ detailed internet search data to examine price and liquidity dynamics of the Dutch housing market. The article shows that the number of clicks on online listed properties proxies demand and the amount of listed properties proxies supply. The created market tightness indicator Granger causes both changes in prices and market liquidity. The results of the panel VAR suggest a demand shock results in a temporary increase in liquidity and a permanent increase in prices. This is in accordance with search and matching models. This paper also provides evidence for loss aversion for current homeowners as prices generally declined during the sample period (2011 - 2013).

Losses on Dutch residential mortgage insurances

Purpose – This paper aims to study the data on losses on mortgage insurance in the Dutch housing market to find the key drivers of the probability of loss. In 2013, 25 per cent of all Dutch homeowners were “under water”: selling the property will not cover the outstanding mortgage debt. The double-trigger theory predicts that being under water is a necessary but not sufficient condition to predict mortgage default. A loss for the mortgage insurer is the result of a default where the proceedings of sale and the accumulated savings for postponed repayment of the principal associated to the loan are not sufficient to repay the loan. Design/methodology/approach – For this study, the authors use a data set on losses on mortgage insurance at a national aggregate level covering the period from 1976 to 2012. They apply a discrete time hazard model with calendar time- and duration-varying covariates to analyze the relationship between year of issue of the insurance, duration, equity, unfortunate events like unempl...

Casametrie: de kunst van het modelleren en het voorspellen van de marktwaarde van meningen

In de afgelopen vijftien jaar is er veel veranderd in de waarderingspraktijk van onroerend goed in Nederland. Modellen spelen een steeds grotere rol in de vaststelling en toetsing van waarden. Ze worden in grote mate gebruikt voor de vaststelling van de WOZ-waarden, voor het meten van de waardeveranderingen van woningcorporatiebezit en bij het verkrijgen van Nationale Hypotheek Garantie. Gezien dit grote belang is het opmerkelijk dat er nauwelijks eisen worden gesteld aan de inhoud van deze modellen. Marc Francke schetst aan welke eisen modellen moeten voldoen vanuit een vastgoed- en econometrisch perspectief. Hij richt zich tevens op de vraag of de Nederlandse woningmarkt is overgewaardeerd. Francke presenteert een econometrisch model dat de woningprijsontwikkeling vanaf 1970 verklaart uit inkomen, rente, inflatie en het financiele vermogen van huishoudens. Hij concludeert dat er momenteel geen sprake is van overwaardering en er geen verdere prijsdalingen te verwachten zijn, zolang de hypotheekrente aftrekbaar blijft.

Internet Search Behavior, Liquidity and Prices in the Housing Market: Internet, Liquidity, Prices in the Housing Market

We employ detailed internet search data to examine price and liquidity dynamics of the Dutch housing market. We show that the number of clicks on properties listed online proxies demand and the number of listed properties proxies supply. From this internet search behavior, we create a market tightness indicator and we find that this indicator Granger causes changes in both house prices and housing market liquidity. The results of a panel VAR suggest that a demand shock results in a temporary increase in liquidity and a permanent increase in prices in urban areas. This is in accordance with search and matching models.