Search results

This paper links the literatures on the life-cycle hypothesis, homeownership, home equity and pensions. Empirically, the focus is on the EU and USA. The paper aims to explore the extent that seniors extract their home equity and discuss the financial instruments available for equity extraction.

The study uses data from the EU and USA to determine homeownership rates, house values and mortgage debt. With these values, the amount of seniors’ home equity is measured for each country. The usage of home equity extraction methods is reported and factors limiting their use are identified.

Seniors’ home equity is a substantial share of their total wealth. Estimates for 2013 are that their home equity equals about €5tn in the USA and over €8tn in large EU countries. The authors find that only a small share of seniors extracts their home equity. While there are supply side constraints in many countries, the evidence suggests that the cause of low extraction rates is the lack of demand. Various reasons for the lack of demand are discussed.

The increasing share of seniors in most countries’ population suggests that there will be increasing pressure on public pension systems. One among many options to address this issue is to impose a wealth test for eligibility, where wealth includes home equity. This study suggests that although home equity is substantial for many seniors, they are reluctant to access the funds.

The paper highlights the importance of home equity in the EU and USA and the factors that affect the primary methods of extraction.

An earlier study found that certain Minnesota state aid programs stimulated city property tax levies to a high degree. If this is accurate, it suggests potentially serious problems with state property tax relief efforts. This article re-examines this question and finds that most aid programs have little direct effect on property tax levies. However, certain aid formulas that reduce the effective price of property taxes do indirectly stimulate property taxes. Therefore, states need to be careful in designing aid programs intended to reduce property taxes.

The role of list price is often discussed in a narrative describing sellers’ preferences or sellers’ price expectations. This paper aims to investigate a set of list price strategies that real estate brokers have available to influence the outcome of the sale, which may be many times self-serving.

By analyzing real estate brokers’ arguments on the choice of the list price level, a couple of hypotheses are formulated with regard to different expected outcomes that depend on the list price. This study empirically tests two hypotheses for the underlying incentives in the choice of list price from the real estate broker’s perspective: lower list price compared to market value leads to the higher sales price, lower list price compared to market value leads to a quicker sale. To investigate the two hypotheses, this paper adopts different methodological frameworks: H1 is tested by running a classical hedonic model, while H2 is tested through a duration model. This study further tests the hypotheses by splitting the full sample into two different price segments: above and below the median list price.

The results show that H1 is rejected for the full sample and for the two sub-samples. That is, contrary to the common narrative among brokers that underpricing leads to a higher sales price, underpricing lower sales price. H2, however, receives support for the full sample and for the two sub-samples. The latter result points to that brokers may be tempted to recommend a list price significantly below the expected selling price to minimize their effort while showing a high turnover of apartments.

Although there are a large number of previous studies analyzing list price strategies in the housing market, this paper is one of the few empirical studies that address the effect of list price choice level on auction outcomes of non-distressed housing sales.

We propose an economic model of housing markets. The model incorporates the macroeconomic relationships between prices, demand and supply. Since vacancy rates are not observable, the demand-supply mismatches are identified using a microeconomic model of search, matching and price formation. The model is applied to data on regional housing markets in England and Wales.

Economic theory combining macroeconomics and microeconomics together with new generation econometric methods for empirical analysis.

The empirical model, estimated for the ten government office regions of England and Wales, validates the economic model. We find that there is substantial heterogeneity across the regions, which is useful in informing housing and land-use policies. In addition to heterogeneity, the model enables us to better understand unrestricted inter-regional spatial relationships. The estimated spatial autocorrelations imply different drivers of spatial diffusion in different regions.

In the nature of other empirical work, the findings are subject to specificities of the data considered here. The understanding of spatial diffusion can also be further developed in future work.

This paper develops a nice way of closing macroeconomic models of housing markets when complete demand, supply and pricing data are not available. The model may also be useful when data are available but with large measurement errors. The model comes together with corresponding empirical methods.

Implications for the housing market and other regional policies are important. These are context-specific, but some implications for housing policy in the UK are provided in the paper as an example.

Unique housing market paper combining both macroeconomic and microeconomic theory as well as both theory and empirics. The rich framework so developed can be extended to much future work.

The purpose of this paper is to estimate that price appreciation for single‐family sales data for the complete population of deed recordings in one metro area using median comparisons is statistically more accurate in capturing local fluctuations than the repeat sale sample approach published by the Federal Housing Finance Agency. The median comparison method becomes the optimal method of choice due to its simplicity and ease in interpretation. The electronic access to court house records means that the complete population of data should be used to extract a community price trend in lieu of samples.

Local deed recordings for residential housing were compared to repeat sale results from the Federal Finance Housing Admin. The goal is to measure housing price appreciation.

Appreciation measured by local deed recordings captures house price variance better than repeat sales taken from mortgage applications.

A median to median comparison with local sales prices should be used in lieu of a national statistic using mortgage applications.

This project provides a more accurate method to assess price appreciation. Repeat sales are not appropriate especially in smaller metro areas.

Every individual and financial institution needs to know the value of its housing asset. The method shown is the best available.

The results have huge implications for the academic community where many regression equations are based on repeat sales.

This paper aims to compare a median‐to‐median estimate of residential price change to the FHFA home price index composed of repeat sales. Further, it seeks to use the complete population of closed sales. The conceptual issue is the use of a “typical” or average comparison through time as opposed to properties where attributes and their marginal prices are held constant.

The paper uses the total population of closed sales rather than a sample. A time series for median price changes is compared to the FHFA time series. The medians for the complete population are the benchmarks, as the median parameter is the true value.

The study finds that the quarterly FHFA price changes do not capture market movements following a major external shock such as a tropical storm. Further, the FHFA data originate in mortgage applications which are not characteristic of the local market. The conclusion is that a median comparison is better, all deed recordings should be used, and repeat sales are not always a valid tool.

Acquiring the data set of all deed transactions involved a budget which may be available to all analysts.

The practical applications are enormous. The results cast doubts on the FHFA home price index and the Case Shiller index. The paper supports the method used by the National Association of Realtors.

All researchers interested in local real estate markets are concerned about the best method to measure changes in local demand and supply market conditions. This project presents a method to use that is sound conceptually and statistically. The reason is that the goal is to measure changes in the “typical”, or average, property over time.

The literature abounds with repeat sale papers. This paper gives an alternative and avoids the many flaws with paired sales. It should have a wide readership.

Researchers have not widely explored design-based factors that govern buildings’ physical properties and human–building interactions. This paper aims to understand the influence of design-related factors on the time-on-market (TOM) of listed houses and, consequently, study the effect of design features on the desirability of a given house.

This research analyzes a dataset of listed houses, provided by the REALTORS^® Association of Edmonton (RAE) and covers a period extending from January 2009 to August 2019, using Cox proportional-hazards regression model to identify building features that influence people purchasing decisions.

The research findings affirm the statistical insignificance of the price on the TOM compared to other design features, such as the construction method, the installed mechanical systems and cladding materials.

The data used in the analysis comes from a single North American region, i.e. Edmonton, Alberta, Canada. Also, the data provided by the RAE includes only records that involve a realtor.

The observations of the research presented in this paper influence the housing market players’ decisions about housing designs, mainly those concerned with building new residential dwellings such as speculative builders and designers.

The research novelty stems from two aspects: the medium used for analysis, i.e. Cox proportional-hazards regression model, which allows considering the listed-but-not-sold units and helps to eliminate the survivorship bias that leads to over-optimistic outcomes; and the assessment of design-related features which allows to understand people’s preferences in design alternatives.

The purpose of this paper is to develop a clear understanding of the features that increase the probability of condos’ sale, with a focus on design-related features.

The present research uses survival analysis (SA) and the Cox proportional-hazards regression (CPHR) to analyze condo sales data provided by the REALTORS® Association of Edmonton (RAE) (Alberta, Canada).

The analysis of the provided data shows that the listed price, building age, appliances and condo fees have less effect on the time a condo spends on the market compared to the condo’s physical features, such as construction material, interior finishing and heating type and source.

The data used in the present research comes from one geographical area (i.e. Edmonton, Canada). Furthermore, the data provided by the RAE does not include any real estate transactions not involving a realtor. Additionally, the present research, owing to its focus on design-related features, does not control features related to the external environment, such as community and transportation proximity.

The findings of the present research help construction practitioners (e.g. architects, builders and realtors) better understand the features that influence condo buyers’ decisions. This knowledge helps to develop designs and marketing strategies that increase the likelihood of selling and decrease the time listed condos spend on the market.

The present research expands our knowledge of the drivers influencing the purchasers’ decisions concerning the building’s physical features that can be controlled during the design stage. Also, analyzing the provided data by using SA and CPHR, as followed in this paper, facilitates the inclusion of records that are listed but not sold, which helps to overcome the survivorship bias and avoid the over-optimism that exists in the present literature.

This study aims to investigate the factors that drive housing loan default in India based on unique micro-level data drawn from a public sector bank's credit files with a national presence in India. The authors address endogeneity in the loan to value ratio (LTV) while deciphering the drivers of default.

The study uses a probit regression approach to analyze the relationship between the probability of default and the explanatory variables. The authors introduce two instrumental variables to address the issue of endogeneity. The authors also add state-level demographic and several other control variables, including an indicator variable that captures the recent regulatory change. The authors’ analysis is based on 102,327 housing loans originated by the bank between January 2001 and December 2017.

The authors find that addressing the endogeneity issue is essential to specify default drivers, especially LTV, correctly. The nature of employment, gender, socio-religious category and age have a distinct bearing on housing loan defaults. Apart from the LTV ratio, the other key determinants of default are the interest rate, frequency of repayment, prepayment options and the loan period. The findings suggest that the population classification of branch location plays a significant role in loan default. The authors find that an increase in per capita income and an increase in the number of employed people in the state, which reflects borrowers' ability to pay by borrowers, reduce the probability of default. The change in the regulatory classification of loan assets by the Reserve Bank of India did not bear the main results.

The non-availability of the house price index in analyzing the default dynamics in the Indian housing finance market for the period covered under the study has constrained our analysis. The applicability of the equity theory of default, strategic default, borrowers' characteristics and personality traits are potential research areas in the Indian housing finance market.

The study's findings are expected to provide valuable inputs to the banks and the housing finance companies to explore and formulate appropriate strategic options in lending to this sector. It has highlighted various vistas of tailor-making housing loan product offerings by the commercial banks to ensure and steady and healthy growth of their loan portfolio. It has also highlighted the regulatory and policy underpinnings to ensure the healthy growth of the Indian housing finance market.

The study provides a fresh perspective on the default drivers in the Indian housing finance market based on micro-level data. In our analysis, the authors find clear evidence of endogeneity in LTV and argue that any attempts to decipher the default drivers of housing loans without addressing the issue of endogeneity may lead to faulty interpretation. Therefore, this study is unique in recognizing endogeneity and has gone deeper in identifying the default drivers in the Indian housing market not addressed by earlier papers on the Indian housing market. The authors also control for the regulatory changes in the Indian housing finance market and include state-level control variables like per capita GDP and the number of workers per thousand to capture the borrowers' ability to pay characteristics.