Modelling Housing Prices using a Present Value State Space Model

This paper introduces a State Space approach to explain the dynamics of rent growth, expected returns and Price-Rent ratio in housing markets. According to the present value model, movements in price to rent ratio should be matched by movements in expected returns and expected rent growth. The state space framework assume that both variables follow an autoregressive process of order one. The model is applied to the US and UK housing market, which yields series of the latent variables given the behaviour of the Price-Rent ratio. Resampling techniques and bootstrapped likelihood ratios show that expected returns tend to be highly persistent compared to rent growth. The Öltered expected returns is considered in a simple predictability of excess returns model with high statistical predictability evidenced for the UK. Overall, it is found that the present value model tends to have strong statistical predictability in the UK housing markets.

Modelling Housing Prices using a Present Value State Space Model

This paper introduces a State Space approach to explain the dynamics of rent growth, expected returns and Price-Rent ratio in housing markets. According to the present value model, movements in price to rent ratio should be matched by movements in expected returns and expected rent growth. The state space framework assume that both variables follow an autoregression process of order one. The model is applied to the US and UK housing market, which yields series of the latent variables given the behaviour of the Price-Rent ratio. Resampling techniques and bootstrapped likelihood ratios show that expected returns tend to be highly persistent compared to rent growth. The filtered expected returns is considered in a simple predictability of excess returns model with high statistical predictability evidence for the UK. Overall, it is found that the present value model tends to have strong statistical predictability in the UK housing markets.

The Time-Series Properties on Housing Prices: A Case Study of the Southern California Market

We examine the time-series relationship between housing prices in eight Southern California metropolitan statistical areas (MSAs). First, we perform cointegration tests of the housing price indexes for the MSAs, finding seven cointegrating vectors. Thus, the evidence suggests that one common trend links the housing prices in these eight MSAs, a purchasing power parity finding for the housing prices in Southern California. Second, we perform temporal Granger causality tests revealing intertwined temporal relationships. The Santa Anna MSA leads the pack in temporally causing housing prices in six of the other seven MSAs, excluding only the San Luis Obispo MSA. The Oxnard MSA experienced the largest number of temporal effects from other MSAs, six of the seven, excluding only Los Angeles. The Santa Barbara MSA proved the most isolated in that it temporally caused housing prices in only two other MSAs (Los Angels and Oxnard) and housing prices in the Santa Anna MSA temporally caused prices in Santa Barbara. Third, we calculate out-of-sample forecasts in each MSA, using various vector autoregressive (VAR) and vector error-correction (VEC) models, as well as Bayesian, spatial, and causality versions of these models with various priors. Different specifications provide superior forecasts in the different MSAs. Finally, we consider the ability of theses time-series models to provide accurate out-of-sample predictions of turning points in housing prices that occurred in 2006:Q4. Recursive forecasts, where the sample is updated each quarter, provide reasonably good forecasts of turning points.

The Impact of Property Condition Disclosure Laws on Housing Prices: Evidence from an Event Study using Propensity Scores

We examine the impact of seller's Property Condition Disclosure Law on the residential real estate values. A disclosure law may address the information asymmetry in housing transactions shifting of risk from buyers and brokers to the sellers and raising housing prices as a result. We combine propensity score techniques from the treatment effects literature with a traditional event study approach. We assemble a unique set of economic and institutional attributes for a quarterly panel of 291 US Metropolitan Statistical Areas (MSAs) and 50 US States spanning 21 years from 1984 to 2004 is used to exploit the MSA level variation in house prices. The study finds that the average seller may be able to fetch a higher price (about three to four percent) for the house if she furnishes a state-mandated seller.s property condition disclosure statement to the buyer. When we compare the results from parametric and semi-parametric event analyses, we find that the semi-parametric or the propensity score analysis generals moderately larger estimated effects of the law on housing prices.

The housing boom and forest fires

This paper provides evidence that the combination of land-use restrictions and anincreasing demand for housing can create incentives to induce forest fires as a means tocircumvent regulation and increase the supply of land available for residential construction.I estimate the effect of the price of housing on the incidence of forest fires using Spanishdata by region for 1991-2005. The results suggest that higher house prices led to asignificant increase in the incidence of forest fires in a region. I also find that the increasedincidence of forest fires led to a subsequent reduction in forest area and an increase in urbanland area. This evidence supports the claims often found in the media that propertyspeculators trying to build in forest land may be behind the recent increases in the incidenceof forest fires in Mediterranean countries.

How well do individuals predict the selling prices of their homes?

Self-reported home values are widely used as a measure of housing wealth by researchers employing a variety of data sets and studying a number of different individual and household level decisions. The accuracy of this measure is an open empirical question, and requires some type of market assessment of the values reported. In this research, we study the predictive power of self-reported housing wealth when estimating sales prices utilizing the Health and Retirement Study. We find that homeowners, on average, overestimate the value of their properties by between 5% and 10%. More importantly, we are the first to document a strong correlation between accuracy and the economic conditions at the time of the purchase of the property (measured by the prevalent interest rate, the growth of household income, and the growth of median housing prices). While most individuals overestimate the value of their properties, those who bought during more difficult economic times tend to be more accurate, and in some cases even underestimate the value of their house. These results establish a surprisingly strong, likely permanent, and in many cases long-lived, effect of the initial conditions surrounding the purchases of properties, on how individuals value them. This cyclicality of the overestimation of house prices can provide some explanations for the difficulties currently faced by many homeowners, who were expecting large appreciations in home value to rescue them in case of increases in interest rates which could jeopardize their ability to live up to their financial commitments.

Defining housing market areas using commuting and migration algorithms. Catalonia (Spain) as an applied case study

In the literature on housing market areas, different approaches can be found to defining them, for example, using travel-to-work areas and, more recently, making use of migration data. Here we propose a simple exercise to shed light on which approach performs better. Using regional data from Catalonia, Spain, we have computed housing market areas with both commuting data and migration data. In order to decide which procedure shows superior performance, we have looked at uniformity of prices within areas. The main finding is that commuting algorithms present more homogeneous areas in terms of housing prices.

Credit borrowing constraints in a DSGE framework: income vs. housing

Credit market in Brazil distinguishes from advanced economies in many aspects. One of them is related to collaterals for households borrowing. This work proposes a DSGE framework, based on Gerali et al.(2010), to analyse one pecularity of Brazillian credit market: payroll-deducted personal loans. To original model, we added the possibility to households contract long term debt and compare to differents types of credit constrains: one based on housing and other based on future income. We callibrate and estimate the model to Brazil, using Bayesian technique. Results show that, in a economy where credit constraints are based on income, responses to shocks appear to be stronger, at first, but dissipate faster. This occurs because income responds quickly to shock than housing prices, so does amount available to loans. In order to smooth consumption, agents compensate lower income and borrowing by increasing working hours, restoring loans and debt in a shorter time.

"Ripple Effects" and Forecasting Home Prices in Los Angeles, Las Vegas, and Phoenix

We examine the time-series relationship between housing prices in Los Angeles, Las Vegas, and Phoenix. First, temporal Granger causality tests reveal that Los Angeles housing prices cause housing prices in Las Vegas (directly) and Phoenix (indirectly). In addition, Las Vegas housing prices cause housing prices in Phoenix. Los Angeles housing prices prove exogenous in a temporal sense and Phoenix housing prices do not cause prices in the other two markets. Second, we calculate out-of-sample forecasts in each market, using various vector autoregessive (VAR) and vector error-correction (VEC) models, as well as Bayesian, spatial, and causality versions of these models with various priors. Different specifications provide superior forecasts in the different cities. Finally, we consider the ability of theses time-series models to provide accurate out-of-sample predictions of turning points in housing prices that occurred in 2006:Q4. Recursive forecasts, where the sample is updated each quarter, provide reasonably good forecasts of turning points.

Analysis of house prices to assess economic impacts of new public transport infrastructure: Madrid metro line 12.

Transportation infrastructure is known to affect the value of real estate property by virtue of changes in accessibility. The impact of transportation facilities is highly localized as well, and it is possible that spillover effects result from the capitalization of accessibility. The objective of this study was to review the theoretical background related to spatial hedonic models and the opportunities that they provided to evaluate the effect of new transportation infrastructure. An empirical case study is presented: the Madrid Metro Line 12, known as Metrosur, in the region of Madrid, Spain. The effect of proximity to metro stations on housing prices was evaluated. The analysis took into account a host of variables, including structure, location, and neighborhood and made use of three modeling approaches: linear regression estimation with ordinary least squares, spatial error, and spatial lag. The results indicated that better accessibility to Metrosur stations had a positive impact on real estate values and that the effect was marked in cases in which a house was for sale. The results also showed the presence of submarkets, which were well defined by geographic boundaries, and transport fares, which implied that the economic benefits differed across municipalities.

Impairment in the mortagage loan portfolio: The example of Banco Popular portugal

A Work Project, presented as part of the requirements for the Award of a Masters Degree in Finance from the NOVA – School of Business and Economics

The effects of cultural heritage on residential property values: Evidence from Lisbon, Portugal

This paper examines the impact of historic amenities on residential housing prices in the city of Lisbon, Portugal. Our study is directed towards identifying the spatial variation of amenity values for churches, palaces, lithic (stone) architecture and other historic amenities via the housing market, making use of both global and local spatial hedonic models. Our empirical evidence reveals that different types of historic and landmark amenities provide different housing premiums. While having a local non-landmark church within 100 meters increases housing prices by approximately 4.2%, higher concentrations of non-landmark churches within 1000 meters yield negative effects in the order of 0.1% of prices with landmark churches having a greater negative impact around 3.4%. In contrast, higher concentration of both landmark and non-landmark lithic structures positively influence housing prices in the order of 2.9% and 0.7% respectively. Global estimates indicate a negative effect of protected zones, however this significance is lost when accounting for heterogeneity within these areas. We see that the designation of historic zones may counteract negative effects on property values of nearby neglected buildings in historic neighborhoods by setting additional regulations ensuring that dilapidated buildings do not damage the city’s beauty or erode its historic heritage. Further, our results from a geographically weighted regression specification indicate the presence of spatial non-stationarity in the effects of different historic amenities across the city of Lisbon with variation between historic and more modern areas.

Omakotitalojen hintadynamiikka Suomessa

Tämän tutkielma tutkii omakotitalojen hintadynamiikkaa Suomessa 1985Q1-2009Q2 välisenä aikana. Tarkoituksena on luoda pitkänaikavälin tasapainomalli sekä lyhyenajan vektorivirheenkorjausmalli, jonka avulla voidaan selvittää asuntojen hintojen mukautumisnopeus kohti tasapainotilaa. Pitkänajan tasapainomallin mukaan omakotitalojen hintaan vaikuttavat eniten kotitalouksien käytettävissä olevat tulot sekä suhteellinen velkaantuneisuus. Koron merkitys jäi mallissa suhteellisen pieneksi. Sekä tulot että velkaantuneisuus vaikuttavat omakotitalojen hintaan positiivisesti. Asuntojen hintojen mukautumisnopeus kohti tasapainotilaa on kohtalaisen nopeaa. Mallin mukaan Suomessa ei ole havaittavissa selvää asuntojen hintakuplaa.

The Brazilian real state market in 2012: robust growth or speculative bubble?

A forte alta dos imóveis no Brasil nos últimos anos iniciou um debate sobre a possível existência de uma bolha especulativa. Dada a recente crise do crédito nos Estados Unidos, é factível questionar se a situação atual no Brasil pode ser comparada à crise americana. Considerando argumentos quantitativos e fundamentais, examina-se o contexto imobiliário brasileiro e questiona-se a sustentabilidade em um futuro próximo. Primeiramente, analisou-se a taxa de aluguel e o nível de acesso aos imóveis e também utilizou-se um modelo do custo real para ver se o mercado está em equilíbrio o não. Depois examinou-se alguns fatores fundamentais que afetam o preço dos imóveis – oferta e demanda, crédito e regulação, fatores culturais – para encontrar evidências que justificam o aumento dos preços dos imóveis. A partir dessas observações tentou-se chegar a uma conclusão sobre a evolução dos preços no mercado imobiliário brasileiro. Enquanto os dados sugerem que os preços dos imóveis estão supervalorizados em comparação ao preço dos aluguéis, há evidências de uma legítima demanda por novos imóveis na emergente classe média brasileira. Um risco maior pode estar no mercado de crédito, altamente alavancado em relação ao consumidor brasileiro. No entanto, não se encontrou evidências que sugerem mais do que uma temporária estabilização ou correção no preço dos imóveis.