Prices Of Housing Units Research Articles

Housing Finance and Markets Dynamics in Tanzania: An Analysis of Cross-sector Linkages

This study examines whether feedback from housing price shocks factored into the availability of mortgage credit in Tanzania between 2008 and 2018. This was done by estimating a Vector Error Correction Model (VECM) with mortgage financing and using three measures of house pricing trends in the luxury, mid-end and economy sub-markets as dependent variables. Results showed that mortgage credit expansion is related to housing price growth in the long-run, but the impact mostly ran from housing price shocks to mortgage growth. In the short-term, changes in price for luxury houses led to a mortgage growth in the first quarter after the shocks, which in turn stimulated changes in housing prices. However, variations on mortgage credit flows had a more significant short-term impact on prices of housing units than it did for houses priced on mortgage credit. The dynamic response between mortgage credit flow and housing prices disappeared when housing price indicators for the economy and mid-end sub-markets were used in the analysis. In addition, both mortgage credit and housing markets were highly persistent, but the effect of previous shocks lasted longer in the mortgage lending process. The paper concludes that the substantial increase in housing prices might be a major concern for policymakers, in particular, because it foreshadows a mortgage crisis.

A multilevel analysis of housing submarkets defined by the municipal boundaries and by the street connections in the metropolitan area: Istanbul

This paper focuses on the performance of the multilevel model within the metropolitan area and explores the submarkets of housing prices at the metropolitan level. In addition to the district and neighborhood segmentation within municipal boundaries, areas using the same street for transportation are segmented into submarkets to reveal the effect of the street on price. A fully-random multilevel model is used for finding out a correlation between the prices of housing units and their location. This study compares four submarket modeling strategies based on the data on housing sale transactions in Istanbul. The first model is a simple hedonic model using ordinary least squares and taken as a city-wide ‘benchmark’ model. Three different types of multilevel models are used for models 2,3 and 4. Model 2 and model 3 use district and neighborhood boundaries, which are respectively predefined as spatial divisions. Model 4 represents the geographically smaller areas that use the same street for transportation. The results reveal that multilevel models with smaller spatial units of geography are capable of improving predictive performance in determining dwelling prices in the metropolitan area and that the sub-regional spatial units that use the same street for transportation have an effect on the spatial dimension of submarket boundaries.

Multiple Linear Regression Model for Estimating the Price of a Housing Unit

This paper uses the respective unit costs, over fifteen (15) years, of selected Housing Unit Major Components (HUMC): cement, iron rods, aluzinc roofing sheets, coral paint, wood and sand, to develop Multiple Linear Regression Model (MLRM) for determining Housing Unit Price (HUP) for one-bedroom and two-bedroom housing units. In the modeling, the Ordinary Least Squares (OLS) normality assumption which could introduce errors in the statistical analyses was dealt with by log transformation of the data, ensuring the data is normally distributed and there is no correlation between them. Minimisation of Sum of Squares Error method was used to derive the model coefficients. The resultant MLRM is: Ŷi MLRM = (X'X)-1 X'Y(xi') where X is the sample data matrix. The specific model for one-bedroom housing unit is loge (HUPMLRM)1-Bed = 1.017 – 2.225 x 10-5 x CC + 2.512 x 10-6 x CS + 6.016 x 10-4 x CIR + 1.985 x 10-4 x CR + 5.694 x 10-4 x CP -7.437 x 10-4 x CW and that for two-bedroom housing unit is loge (HUPMLRM)2-Bed = 5.760 – 7.501 x 10-7 x CC + 2.935 x 10-6 x CS + 1.898 x 10-3 x CIR + 6.695 x 10-4 x CR - 9.157 x 10-3 x CP +6.136 x 10-3 x CW, where CC, CS, CIR, CR, CP and CW are costs of the total quantity of cement, sand, iron rods, roofing, paint and wood respectively. The MLRM was validated by using it to estimate the known HUP in the 15.5th year. From the results, the percentage absolute deviations of the estimated HUP from the known HUP are 1.27% and 2.02% for one-bedroom and two-bedroom housing units respectively, which are satisfactory. The novel approach presented in this paper is a valuable contribution to the body of knowledge in modeling. Keywords: Multiple Regression Analysis, Housing Unit Major Components, Housing Unit Price

Short Sales and Residential Real Estate Prices

Previous research identifies a negative externality associated with mortgage foreclosures. Houses in the neighborhood surrounding a foreclosed property sell for less, all things being equal. This paper explores the effect of short sales on condominium prices in downtown San Diego, California. The neighborhood in this research is defined as the building in which the condominium is located. The results suggest that not only is there a short sale “discount”, but buildings in which there are a large number of short sales also experience a general decline in market prices of housing units.

Systemic risk from real estate and macro-prudential regulation

There is considerable evidence that boom and bust cycles in real estate are the primary cause of financial crises. This paper develops a model of real estate pricing based on rational behaviour with two regimes. In 'normal times' prices of housing units are determined by the consumers who live in them and are equal to the discounted stream of housing services. In 'boom and bust times' speculators find it profitable to borrow from banks and enter the market. There is an agency problem because banks are unable to fully assess the risk that the speculators are taking and this leads to risk shifting and asset substitution. The result is a bubble in real estate prices in that they are higher than the discounted stream of housing services during the boom phase. This model is then used as the basis for analysing macro-prudential polices designed to prevent the occurrence of such bubbles. These measures include monetary policy, fiscal tools such as real estate transfer taxes, and annual real estate taxes and banking regulation such as restrictions on loan-to-value ratios, countercyclical capital requirements and loan provisions.

HOPE VI: An Analysis to Determine the HOPE VI Program's Influence on Home Sales

This article will examine the original Housing Opportunity for People Everywhere (HOPE) VI sites in the cities of Atlanta, Baltimore, Charlotte, and Washington, D.C., to determine if surrounding home sales increase after its completion. The author utilizes an Adjusted Interrupted Time Series (AITS) method, first developed by Galster, Temkin, Walker, and Sawyer (2004), to examine the effects of the HOPE VI program on local real housing prices (which is utilized as a proxy for local economic development in this paper and in previous urban economic research). The author found that the prices of housing units in the HOPE VI concentric rings of a half mile and 1 mile around the HOPE VI site increased after completion. The results also showed that the housing price trend around the HOPE VI site began to increase after the completion date of each site. The price trend of housing units in the inner ring of the HOPE VI sites grew more than those in the outer ring. Therefore, the results indicate that the HOPE VI site has a significant and positive influence on housing prices.