House Prices Research Papers - Academia.edu (original) (raw)

2025, Social Science Research Network

2025

This paper presents a methodology for detecting asset price booms and busts using nonparametric quantile regressions. The method consists in estimating the distribution of real stock prices as a function of fundamental determinants of stock returns, namely real economic activity and real interest rates. It is shown that changes in fundamentals affect not only the location but also the shape of the conditional distribution of stock prices. Asset price booms and busts are identified as realizations on the tails of that distribution. Then we use several indicators to analyse the behaviour of money and credit around the boom and bust episodes.

2025, Social Science Research Network

We estimate empirically the effect of immigration on house prices and residential construction activity in Spain over the period 1998-2008. This decade is characterized by both a spectacular housing market boom and a stunning immigration wave. We exploit the variation in immigration across Spanish provinces and construct an instrument based on the historical location patterns of immigrants by country of origin. The evidence points to a sizeable causal effect of immigration on both prices and quantities in the housing market. Between 1998 and 2008, the average Spanish province received an immigrant inflow equal to 17% of the initial working-age population. We estimate that this inflow increased house prices by about 52% and is responsible for 37% of the total construction of new housing units during the period. These figures imply that immigration can account for roughly one third of the housing boom, both in terms of prices and new construction.

2025, Journal of Environmental Economics and Policy

Incorporating spatial econometric tools in Hedonic Pricing (HP) models for environmental valuation has become the standard approach in the literature. The effect of house prices on other house prices is taken into account and usually measured by distance or contiguity in spatial weight matrices. Disaggregate house sale datasets are composed from observations each at a specific location and time. Nevertheless, the symmetric spatial weight matrices commonly employed in HP studies ignore the temporal dimension in disaggregate house sale data. Thus not only are previous house sales taken to affect subsequent house prices, but so do future house sales. However, information does not travel backwards in time; hence there is a clear theoretical impossibility of actual future prices affecting current/past prices. Estimates derived from HP models where spatial dependence is incorrectly specified or ignored will exhibit inaccuracies. This paper proposes an alternative specification of spatial weights in HP that includes spatial effects on each sale price only from preceding house sales. The temporal aspect of spatial effects is then developed further by specifying a time decay rate to capture the diminishing effect over time of preceding sale prices to succeeding house prices. This novel specification of spatial weight matrices is shown to have a significant effect on estimates of house price depreciation from aircraft noise. Monetary values of aircraft noise externality are successfully derived from the HP models for Athens Airport.

2025, RePEc: Research Papers in Economics

This paper studies whether gravity model parameters estimated in one geographic area can give reasonable predictions of commuting flows in another. To do this, three sets of parameters are estimated for geographically proximate yet separate regions in south-west Norway. All possible combinations of data and parameters are considered, giving a total of nine cases. Of particular importance is the distinction between statistical equality of parameters and 'practical' equality i.e. are the differences in predictions big enough to matter. A new type test based on the Standardised Root Mean Square Error (SRMSE) and Monte Carlo simulation is proposed and utilised.

2025, Satyanarayana Ballamudi

In the rapidly evolving landscape of technology-driven commerce, laptops have become indispensable for both personal and professional applications, with a vast array of models presenting varied specifications and features. The intricate interplay of hardware configurations and pricing frameworks underscores the necessity for robust predictive models that empower consumers and manufacturers to make well-informed choices. This study delves into the critical challenge of accurately forecasting laptop prices by evaluating three machine learning methodologies: Linear Regression (LR), Histogram Gradient Boosting Regression (HGBR), and XGBoost Regression (XGBR). The research's importance is rooted in its capacity to refine pricing strategies, bolster market efficiency, and provide consumers with deeper insights into the value dynamics associated with different laptop specifications. The study leveraged an extensive dataset comprising 1,303 laptop entries, each characterized by 11 pivotal attributes encompassing processor type, RAM, storage capacity, screen dimensions, and graphical performance. Analytical techniques encompassed correlation assessment, feature significance determination, and comparative evaluation of model efficacy, employing key performance indicators such as the R² coefficient, Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). XGBoost demonstrated a clear dominance over other predictive models, securing an R² value of 0.93559 on training data and 0.77524 on testing data. This superiority was further underscored by its markedly lower error margins, with an RMSE of 9,334.9 for training data, starkly contrasting the significantly higher 23,506.3 observed in Linear Regression. A thorough correlation analysis pinpointed RAM and processor specifications as the most decisive variables influencing price determination. The study asserts that ensemble learning methodologies, particularly XGBoost, represent the most dependable strategy for forecasting laptop prices. Nonetheless, the research highlights key areas for refinement, especially in narrowing the discrepancy between training and testing performance. These insights hold substantial implications for stakeholders in the laptop industry, paving the way for the advancement of more sophisticated predictive frameworks. Furthermore, the study enriches the broader discourse on consumer electronics pricing, emphasizing the transformative role of machine learning in optimizing market dynamics and strategic decision-making.

2025, Journal of Real Estate Finance and Economics

Weighted repeat sales house price indices have become one of the primary indicators used to identify housing market conditions and to estimate the amount of equity homeowners have gained through house price appreciation. The primary reason for the acceptance of this methodology is that it derives a location specific (typically, census division, state or metropolitan area) average change in house prices from repeated observations of individual house prices. It is this repeat attribute that allows repeat sales price indices to claim that it is a preferable index which does a better job of holding quality constant. The amount of time between the two observed prices for a single property is determined by when the home transacts. Some homes transact twice in a period of months and others do not transact for decades. It is likely that individual house price appreciation rates vary from the mean appreciation rate, as estimated by the index, in a systematic fashion. In general, the longer the time between transactions the more variance there is in individual house price appreciation. This paper extends this concept to include new dimensions. For instance, houses that appreciate faster than the mean, as estimated by the index for that location, may experience a different variation structure than homes that appreciate slower. This process can be viewed as an asymmetric treatment of the variance of house price appreciation around the estimated index. In addition, the variance of expensive and affordable homes may also be different and time varying. This paper finds evidence that adding the dimensions of price tiers and asymmetry to the variance estimate has merit and does affect the estimated index as well as homeowner equity estimates. Homeowner equity estimates are especially sensitive to these added dimensions because they depend on both the revised index and the estimated variances, which are specific to each dimension considered-time between transaction, asymmetry, and price tier.

2025

M B E R 1 REVIEW JANUARY/FEBRUARY 2006 Federal Reserve Bank of St. Louis

2025

This paper presents a framework for the construction of quarterly residential real estate price indices (RREPIs) for Jamaica. In this study, a rolling window hedonic pricing approach is used to create the RREPIs using mortgage transaction and assessment information on dwellings across all 14 parishes of Jamaica collected by the National Housing Trust (NHT). Additionally, two subindices are computed for the most active NHT geographic markets, St. Catherine and Kingston & St. Andrew. The RREPIs show that prices have generally been trending upwards over the period December 2008 to June 2016. Furthermore, activity in the two most active geographic markets largely drive the outturn in the index for Jamaica. Overall, these results have important implications for the development of macro-prudential policy tools for the mitigation of asset price volatility in Jamaica. JEL Classification: C43, C51, O18

2025

This paper presents a framework for the construction of a residential real estate price index for Jamaica. This real estate price index will consist of a sales value-weighted aggregation of price sub-indices across geographical regions or zones. In this study, a hedonic price imputation model for housing in the parishes of Kingston & St. Andrew is estimated using mortgage transaction and assessment information on dwellings over the period 2003 to 2007 collected by the National Housing Trust. This approach allows for the efficient use of estimated marginal contributions for each real estate characteristic to construct a price index without any further econometric updating, subject to stability tests, for typically four to five years.

2025, Journal of Public Economics

We thank Tom Davidoff, Seth Freedman, Phil Levine, Tim Moore and participants at the University of Maryland applied micro lunch for helpful comments on this project. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research. NBER working papers are circulated for discussion and comment purposes. They have not been peerreviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications.

2025, Lapai Journal of Economics

With recourse to data obtained from the online multiple listing services (MLS) of the Nigerian Property Centre, this study evaluated the efficiency of sale of distressed properties in Lagos city using a 6-month interval of observations protocol, comprising of listings of less than 6 months, 7 to 12 months, 13 to 18 months, and 19 to 24 months respectively. Time-on-the-market (TOM) in this study was perceived to be the duration (in months) from the listing date to the date of data collection. Results of selected parametric and non-parametric statistical tests indicated that there was an insignificant difference in the observation of distressed properties that have remained on the market for at most 6 months and those with marketing time beyond 6 months. Similarly, properties listed for distressed sales in Lagos city were found to have continuously attracted lower purchase rates in the same manner as the non-distressed properties. These results avowed the phenomenon of inefficient sale of distressed properties in Lagos city, Nigeria. It was recommended by the study that individuals and institutions in property brokerage business in Lagos city should conduct a risk assessment of the diminishing likelihood of distressed sales and the incremental losses associated with lengthy listing periods.

2025, HAL (Le Centre pour la Communication Scientifique Directe)

Bridging southern and northern Europe, France presents very diversified urban landscapes, combining vernacular cores, modernist developments, sprawling suburbia and very specific exurbs. Traditional urban and rural landscapes were different among French cultural regions, whereas more recent ones are apparently more homogeneous throughout the country. Research in urban morphology and heritage conservation produced considerable knowledge of French traditional urban forms. On the contrary, attention to more recent forms and non-residential areas, as well as to their contribution to contemporary French cityscapes is more recent. The identification and characterization of France's urban fabrics and morphological regionalization of French cities remained for long a complex task, traditionally based on in-depth assessments, restricted to specific historical, geographical and cultural contexts. Recent advancements in morphometric analysis propose innovative computeraided protocols overcoming these limits. Among them, Multiple Fabric Assessment is a Bayesian streetscapebased urban morphometric protocol for morphological regionalization. First presented at ISUF2017, MFA has already been applied to several urban areas in different sociocultural contexts and geographical scales. MFA has been further developed and upscaled to analyse and compare wider study areas. The paper presents its implementation for four metropolitan areas of France, around the cities of Lyon, Marseille-Aix-en-Provence, Lille-Roubaix-Tourcoing, and Nice-Cannes-Antibes, allowing a multiscale comparative analysis of French urban forms. A shared taxonomy of urban fabric types (morphotypes) for the different case studies is proposed. The outcome of these analyses is a first contribution to a national atlas of morphologically regionalized metropolitan areas.

2025, HAL (Le Centre pour la Communication Scientifique Directe)

comme ceux de couvertures du sol, sont des taux et posent un problème de variabilité avec la taille de la population mère. La correction empirique Bayésienne utilisé en épidémiologie, où la population mère serait la surface de l'unité spatiale, semble mal s'appliquer à la morphologie urbaine, car celle-ci influence le découpage spatial. Des nouvelles corrections Bayésiennes sont ainsi proposées et testées sur les paysages urbains de la Côte d'Azur. Une correction utilisant une fonction sublinéaire de la surface se montre plus apte à réduire l'hétéroscédasticité des taux et à identifier les concentrations de tissu pavillonnaire. ABSTRACT. A set of morphological indicators is proposed to identify urban fabric using spatial clustering. The LINCS approach is preferred to classical LISA in order to better integrate the point of view of pedestrians moving in the city. Some morphological indicators like land coverage are rates and this poses the well-known problem of rate variability with the base population size. Classical empirical Bayesian correction used in epidemiology, with the spatial unit surface area as base population, seems unfit to the analysis of urban morphology, as spatial units depend from morphological phenomena. New empirical Bayesian corrections are thus proposed and tested on the case study of urban landscapes of the French Riviera. A new Bayesian correction which is a sublinear function of the unit surface proves better able to reduce rate heteroscedasticity and to identify hotspots of individual houses.

2025, HAL (Le Centre pour la Communication Scientifique Directe)

This paper presents a multi-scale detection of urban fabric types through Multiple Fabric Assessment in Marseille, France's second city. MFA is a computer-aided streetscape-based urban morphometric protocol for morphological regionalization of large urban areas. First presented at ISUF 2017, MFA has already been successfully applied to the metropolitan areas of the French Riviera, Osaka and Brussels. The protocol is first applied to the central city, and then to the much larger metropolitan area around Marseille and Aix-en-Provence, stretching over 7000 km2 and home to 2.6 million inhabitants. In both cases, MFA detects eight well-defined families of urban fabrics with clear morphological specificities. The change of spatial extent has nevertheless consequences on the analysis results. On the one hand, urban fabric types detected at the two scales show a precise pattern of correspondences. On the other, each scale allows a finer description of its most preponderant morphological regions, detecting more specific types which are bundled together at the other scale. This is the case for the traditional urban fabrics of the compact city at the municipal scale, and for the suburban fabrics at the metropolitan scale. Accepting some generalisation, the urban fabric types detected by MFA are able to account for the variety of urban forms identified in 36 urban fragments by Marseille's metropolitan planning agency through classical morphological analysis. Beyond the different grain of the analyses (streetscapes vs urban blocks), expert-based and computer-aided classifications are in good agreement. Allowing comprehensive multiscale analyses of urban fabrics for the whole metropolitan area of Marseille, MFA showed the patchwork nature of its 20 th century developments and put in perspective the overstated fragmentation of the 19 th century urbanisation. Participating to the emerging field of urban morphometrics, MFA opens the way to wider comparative analysis at the national and international levels.

2025, HAL (Le Centre pour la Communication Scientifique Directe)

2025, Information Processing and Management of Uncertainty in Knowledge-Based Systems

Prompted by an application in the area of human geography using machine learning to study housing market valuation based on the urban form, we propose a method based on possibility theory to deal with sparse data, which can be combined with any machine learning method to approach weakly supervised learning problems. More specifically, the solution we propose constructs a possibilistic loss function to account for an uncertain supervisory signal. Although the proposal is illustrated on a specific application, its basic principles are general. The proposed method is then empirically validated on real-world data.

2025, HAL (Le Centre pour la Communication Scientifique Directe)

2025, FRBSF Economic Letter

The Federal Reserve's current large-scale asset purchase program, dubbed "QE2," has a precedent in a 1961 initiative by the Kennedy Administration and the Federal Reserve known as "Operation Twist." An analysis finds that four of six potentially market-moving Operation Twist announcements had statistically significant effects and that the program cumulatively caused a significant but moderate 0.15 percentage point reduction in longer-term Treasury yields. These results can be used to estimate QE2's effects. John F. Kennedy was elected president in November 1960 and inaugurated on January 20, 1961. The U.S. economy had been in recession for several months, so the incoming Administration and the Federal Reserve wanted to lower interest rates to stimulate the weak economy. However, Europe was not in a recession at the time and European interest rates were higher than those in the United States. Under the Bretton Woods fixed exchange rate system then in effect, this interest rate differential led cross-currency arbitrageurs to convert U.S. dollars to gold and invest the proceeds in higher-yielding European assets. The result was an outflow of gold from the United States to Europe amounting to several billion dollars per year, a very large quantity that was a source of extreme concern to the Administration and the Federal Reserve.

2025, Tijdschrift voor Economische en Sociale Geografie

ABSTRACTEven in a small country such as the Netherlands, spatial variations in house prices are considerable. This paper addresses the question about the extent to which these variations in house prices can be explained by differences in physical, social and functional characteristics of the residential environment. Data on the prices and characteristics of houses were linked with a variety of attributes of the residential environment. We used hedonic price modelling to derive different models of property prices from which the contribution of the characteristics of the residential environment were estimated. It is demonstrated that regional house price variations can indeed largely be explained by characteristics of the residential environment. An important factor in these regional price differences is the accessibility to employment opportunities.

2025

We exploit the structure of the Clean Air Act to provide new evidence on the capitalization of total suspended particulates (TSPs) air pollution into housing values. This legislation imposes strict regulations on polluters in "nonattainment" counties, which are defined by TSPs concentrations that exceed a federally set ceiling. TSPs nonattainment status is associated with large reductions in TSPs pollution and increases in county-level housing prices. When nonattainment status is used as an instrumental variable for TSPs, we find that the elasticity of housing values with respect to particulates concentrations range from -0.20 to -0.35. These estimates of the average marginal willingness-to-pay for clean air are far less sensitive to model specification than cross-sectional and fixed effects estimates, which occasionally have the "perverse" sign. We also find modest evidence that the marginal benefit of pollution reductions is lower in communities with relatively high pollution levels, which is consistent with preference-based sorting. Overall, the improvements in air quality induced by the mid-1970s TSPs nonattainment designation are associated with a $45 billion aggregate increase in housing values in nonattainment counties between 1970 and 1980.

2025, Social Science Research Network

economists, whose research aims to provide answers to the global labor market challenges of our time. Our key objective is to build bridges between academic research, policymakers and society. IZA Discussion Papers often represent preliminary work and are circulated to encourage discussion. Citation of such a paper should account for its provisional character. A revised version may be available directly from the author.

2025, Review of Economic Dynamics

This paper studies the determinants of housing tenure choice and the differences in the cost of housing services across households in an overlapping generations model with household-specific uninsurable earnings risk and housing prices that vary over time. We model houses as illiquid assets that provide collateral for loans. To analyze the impact of preferential housing taxation on the tenure choice, we consider a tax system that mimics that of the U.S. economy in a stylized way. We find that a mixture of idiosyncratic earnings uncertainty, house price risk, down payments and transaction costs are needed for the model to deliver life cycle patterns of homeownership and portfolio composition similar to those found in the data. Through simulations, we also show that a rental equivalence approach (relative to a user cost approach) overestimates the mean unit cost of housing by approximately 3 percent.

2025, Journal of Monetary Economics

We show that homeowners are able to maintain a high level of consumption following job loss or disability in periods of rising house values. However, the consumption drop for consumers who simultaneously lose their job and equity in their houses is substantial. Using data from the Panel Study of Income Dynamics, we verify that homeowners smooth consumption more than renters, and that consumption smoothing improves when houses appreciate in the area of residence. We calibrate and simulate a model of endogenous homeownership and home-equity loans, and show that the model is able to reproduce the patterns in the data quite well.

2025, Working Papers

This paper examines whether rising house prices immediately prior to children entering their college years impacts their intergenerational earnings mobility and/or educational outcomes. Higher house prices provide homeowners, especially liquidity constrained ones, with additional funding to invest in their children's human capital. The results show that a 1 percentage point increase in house prices, when children are 17-years-old, results in roughly 0.8 percent higher annual income for the children of homeowners, and 1.2 percent lower annual income for the children of renters. Additional analysis shows that the children who benefit the most from rising house prices are those whose parents are liquidity constrained homeowners. Rising house prices also make homeowners' children more likely to graduate from college and have less noncollateralized debt when young adults. Both of these results are consistent with rising house prices enabling parents to invest more in their children.

2025

In most developed countries, housing receives preferential tax treatment relative to other assets. In particular (i) the housing services provided by owner-occupied housing (generally referred to as imputed rents) are untaxed and (ii) mortgage interest payments reduce taxable income. The potential economic distortions resulting from the unique treatment of housing may be substantial, especially in light of the fact that residential capital accounts for more than half of the assets in the U.S. In particular, this tax treatment distorts the households' portfolio composition, their saving rates and their tenure choice. In this paper we build a general equilibrium model populated by heterogeneous agents subject to idiosyncratic risk. We use this framework to quantitatively assess the macroeconomic and distributional distortions introduced by this preferential tax treatment. We also study the effects of alternative tax schemes which could correct the current system's bias. " Correspondence to Maria J. Luengo-Prado at m.luengo0neu.edu. We would like to thank as many people as possible. Diaz thanks the Direccion General de Investigacion, project BEC2001-1653, for financial support. Luengo-Prado is indebted to the DirecciOn General de InvestigaciOn, project BEC2000-0173, for financial support. They both are grateful to the FundaciOn Ramon Areces for financial support. All comments are welcomed.

2025, Computers, Environment and Urban Systems

Geographical kernel weighting is proposed as a method for deriving local summary statistics from geographically weighted point data. These local statistics are then used to visualise geographical variation in the statistical distribution of variables of interest. U nivariate and bivariate summary statistics are considered, for both moment-based and order-based approaches. Several aspects of visualisation are considered. F inally, an example based on house price data is presented.

2025, Landscape and Urban Planning

This paper analyses the link between housing prices and urban green areas endowments using the hedonic technique as methodological approach. Together with the conventional variables used to explain housing prices, three environmental variables are considered: the existence of views of a park or a public garden, the distance from the dwelling to its nearest green area and the size of that open space. The sample is made up of 810 observations gathered from the city of Castellón (Spain). Results show housing size to be the most relevant variable on price. As far as the hedonic variables are concerned, there is an inverse relationship between the selling price of the dwelling and its distance from a green urban area.

2025, Satyanarayana Ballamudi

2025, Journal of Housing Economics

This paper examines the long-term impact and short-term dynamics of macroeconomic variables on international housing prices. Since adequate housing market data are generally not available and usually of low frequency, a panel cointegration analysis consisting of 15 countries over a period of thirty years is applied. Pooling the observations allows us to overcome the data restrictions which researchers face when testing long-term relationships among single real estate time series. This study does not only confirm results from previous studies but also allows for a comparison of single country estimations in an integrated equilibrium framework. The empirical results indicate positive effects on house prices arising from an increase in economic activity, construction costs, and the short-term interest rate and negative effects stemming from an increase in the long-term interest rate. Deviations from the long-term equilibrium result in a dynamic adjustment process that can take several years.

2025, SSRN Electronic Journal

for comments on the paper. We also thank James Newell for providing us with data and assistance in creating local labour market boundaries.

2025

I develop a flexible and estimable equilibrium model that jointly considers location decisions of heterogeneous agents across space, and their optimal portfolio decisions. Merging continuous-time asset pricing with urban economics models, I find a unique sorting equilibrium and derive equilibrium house and asset prices in closed-form. Risk premia for homes depend on both aggre-gate and local idiosyncratic risks, and equilibrium returns for stocks depend on their correlation with city-specific income and house price risk. In equilibrium, very risk-averse households do not locate in risky cities although they may have a high productivity match with those cities. I estimate a version of this model using house price and wage data at the metropolitan area level and provide estimates for risk premia for different cities. The estimated risk premia imply that homes are on average about 20000cheaperthantheywouldbeifownerswererisk−neutral.Thisestimateisover20000 cheaper than they would be if owners were risk-neutral. This estimate is over 20000cheaperthantheywouldbeifownerswererisk−neutral.Thisestimateisover100000 for volatile ...

2025, Cahiers de recherche

Mortgage indebtedness has risen considerably in Canada in recent years, pushing households' debt-to-income ratio to an all time high. In order to identify what variables explains these changes in the stock of debt, we analyze the inflow and outflow of mortgage financing. We show that the number of new mortgage loans is mostly influenced by nominal interest rates while their average value reacts only to housing price. As to the outflow of debt repayment it is sensitive to more variables. Since housing price is also strongly influenced by nominal interest rate, we show that the main driving force towards higher Canadian households' mortgage debt is the reduction in nominal interest rate. .H\ZRUGV mortgage demand, indebtedness, housing. -(/ : G21, R21

2025, International Journal of Housing Markets and Analysi

While air pollution is widely recognized as a factor affecting the real estate market, gaps remain in understanding spatial dependencies, valuation biases, and heterogeneous effects of air pollution on housing markets. Traditional OLS models often overestimate its impact by ignoring spatial spillovers and endogeneity, while limited research explores how Two-Stage Least Squares (2SLS) improves marginal willingness to pay (MWTP) estimation. This study applies spatial econometric models (SLM, SEM) and 2SLS to assess air pollution's impact on housing prices in Thailand. The results confirm significant price reductions, with OLS overestimating the marginal implicit price (MIP) compared to spatial models. The average MIP per house is 39,838 THB ($1,138), varying across models, while the estimated Compensating Surplus (CS) averages 568,078 THB ($16,578) per household, reflecting the economic burden of air pollution. The Hausman test confirms endogeneity, while the Hansen J-Test validates instrumental variables (industrial facility density, rainfall). These findings highlight substantial economic losses from air pollution, emphasizing the need for spatially aware valuation techniques in real estate pricing and policymaking.

2025, Empirical Economics

The recent boom in house prices in many countries during the COVID-19 pandemic and the possibility of household financial distress are of concern among some central banks. We revisit the empirical modelling of house prices and household debt with a policy-oriented perspective using Norwegian data over the last four decades within the cointegrated VAR model. Our findings suggest, in line with previous work, a longrun mutually reinforcing relationship between these financial magnitudes, and thus the potential for the build-up of financial instabilities and spillover effects to the real economy. Applying a policy control analysis, we find that both house prices and debt are controllable magnitudes to some pre-specified target levels through the mortgage interest rate, which enables the central bank to reduce large fluctuations and bubble tendencies in the housing market. The present control analysis thus provides some useful policy implications from empirically relevant representations of two important financial factors entering the decision process of the policy maker.

2025, Empirical Economics

The recent boom in house prices in many countries during the COVID-19 pandemic and the possibility of household financial distress are of concern among some central banks. We revisit the empirical modelling of house prices and household debt with a policy-oriented perspective using Norwegian data over the last four decades within the cointegrated VAR model. Our findings suggest, in line with previous work, a long-run mutually reinforcing relationship between these financial magnitudes, and thus the potential for the build-up of financial instabilities and spillover effects to the real economy. Applying a policy control analysis, we find that both house prices and debt are controllable magnitudes to some pre-specified target levels through the mortgage interest rate, which enables the central bank to reduce large fluctuations and bubble tendencies in the housing market. The present control analysis thus provides some useful policy implications from empirically relevant representation...

2025, RePEc: Research Papers in Economics

We adapt nonnegative garrote method to perform variable selection in nonparametric additive models. The technique avoids methods of testing for which no reliable distributional theory is available. In addition it removes the need for a full search of all possible models, something which is computationally intensive, especially when the number of variables is moderate to high. The method has the advantages of being conceptually simple and computationally fast. It provides accurate predictions and is effective at identifying the variables generating the model. For illustration, we consider both a study of Boston housing prices as well as two simulation settings. In all cases our methods perform as well or better than available alternatives like the Component Selection and Smoothing Operator (COSSO).

2025, IMF Working Papers

This Working Paper should not be reported as representing the views of the IMF.

2025

In recent years, household debt levels among the industrialized countries have increased heavily. The Swedish central bank has had concerns about this increase and its macroeconomic effect. Partly this is due to the fact that private consumption might have been inflated by borrowed money. As some perceive household debt as a stimulant, others fear the consequences of over-indebtedness. This paper has examined the relationship between household debt and private consumption in Sweden, Spain, France and the UK. The data range from 1988 to 2012. Three time-series models have been specified with the permanent-income hypothesis as the theoretical framework. The models show that household debt levels have a significant impact on private consumption in France and the UK. However, in the case of Sweden and Spain no significant relationship is found. In the conclusion these insignificant results are discussed to be a problem of the specification of the mode. Further variables could have improved the results. .

2025, Annals of Economics and Finance

Households across Europe are struggling with a double crisis-the worst inflation shock since the World War II and a sudden correction in house prices. There is a rich literature on how housing price cycles affect consumer spending, finding mixed results with a wide range of consumption responses to changes in housing wealth. In this paper, using quarterly data on 20 countries in Europe over the period 1980-2023, we analyze the dynamic relationship between inflation-adjusted house prices, disposable income and consumer spending and obtain statistically significant and economically intuitive results. Household consumption responds positively and swiftly to changes in real house prices and gross disposable income as expected. Quantitatively, we find that private consumption falls by 0.13 percentage points on average for one percent decrease in real house prices and 0.02 percentage points for a one percent decrease in real gross disposable income in the first quarter after the shock, plateauing after six and four quarters, respectively.

2025, Journal of Real Estate Research

Factors external to a home's characteristics may influence the sales price. This analysis focuses on Bellingham, Washington, because of several influences including the Canadian economy and nonresidents. First estimated is a constant-quality Bellingham housing price index, which is used as the dependent variable in a reduced-form model of market price to estimate the impact of the exchange rate. The analysis (1984-94) suggests that a 10% rise in the exchange rate leads to a 7.7% rise in Bellingham home prices. Additionally, in 1990, non-county buyers paid 4% to 6% more than county residents and non-county sellers received 6% to 8% less.

2025, Journal of Real Estate Research

2025, The Journal of Real Estate Finance and Economics

2025, Przegląd Statystyczny. Statistical Review

2025

El objetivo fue analizar las emisiones de Dioxido de Nitrogeno, generados por el campo automotor en el sector del Terminal Terrestre de la ciudad de Riobamba durante el segundo trimestre del ano 2016. La zona de estudio fue el Terminal Terrestre de la ciudad de Riobamba y sus alrededores, debido a su gran flujo vehicular. Se utilizo tubos muestreadores que fueron colocados en los postes electricos a 3 metros sobre el suelo, los puntos fueron seleccionados aplicando la tecnica del muestreo aleatorio simple. Los tubos muestreadores se encargan de recolectar el Dioxido de Nitrogeno emitido por los vehiculos que transitan por la localidad. Despues de retirar los tubos muestreadores, se llevaron al laboratorio a ser coloreados para luego ser puestos en el espectrofotometro para calcular su absorbancia y por ende encontrar la concentracion de Dioxido de Nitrogeno presente en las muestras. Los resultados obtenidos del estudio nos muestras valores de 215,3060451µg/m³, 206,2802582 µg/m³, 182...