CONSUMPTION AND INVESTMENT VALUES IN HOUSING PRICE: A REAL OPTIONS APPROACH

Homeowners can be viewed as the put option holders who can sell housing to lenders when the housing price is lower than its mortgage value and sell houses when the housing price rises above a certain threshold. On the basis of the theory of investment under uncertainty, we model the housing value from the perspective of houseowners who can choose to either live in their houses or switch houses for comfort improvement and price appreciation. We can decompose the housing value into consumption and investment values by exploring parameters affecting housing value and decision making of houseowners. We find that the proportion of investment value to housing value increases with the volatility of the housing market, indicating the possible formation of housing bubbles. In addition, the comfort and utility provided by housing are critical for homeowners to decide whether to sell their houses. The analysis provides policymakers and market participants in the real estate market with insights into the price formation of real estate.

Predicting the rental value of houses in household surveys in Tanzania, Uganda and Malawi: Evaluations of hedonic pricing and machine learning approaches

Housing value is a major component of the aggregate expenditure used in the analyses of welfare status of households in the development economics literature. Therefore, an accurate estimation of housing services is important to obtain the value of housing in household surveys. Data show that a significant proportion of households in a typical Living Standard Measurement Survey (LSMS), adopted by the Word Bank and others, are self-owned. The standard approach to predict the housing value for such surveys is based on the rental cost of the house. A hedonic pricing applying an Ordinary Least Squares (OLS) method is normally used to predict rental values. The literature shows that Machine Learning (ML) methods, shown to uncover generalizable patterns based on a given data, have better predictive power over OLS applied in other valuation exercises. We examined whether or not a class of ML methods (e.g. Ridge, LASSO, Tree, Bagging, Random Forest, and Boosting) provided superior prediction of rental value of housing over OLS methods accounting for spatial autocorrelations using household level survey data from Uganda, Tanzania, and Malawi, across multiple years. Our results showed that the Machine Learning methods (Boosting, Bagging, Forest, Ridge and LASSO) are the best models in predicting house values using out-of-sample data set for all the countries and all the years. On the other hand, Tree regression underperformed relative to the various OLS models, over the same data sets. With the availability of abundant data and better computing power, ML methods provide viable alternative to predicting housing values in household surveys.

THE MAIN DIRECTIONS OF SOLVING THE SCIENTIFIC PROBLEM OF FORMING AND EVALUATING THE VALUE OF A PROFESSIONAL PORTFOLIO OF RENTAL HOUSING

The article presents a comparative fundamental and statistical analysis and forecast of the professional rental housing segment development in Russia. According to the authors, the rental housing market development and the growth of its capitalization will lead to the reduction of system risks of the Russian market by limiting the volatility of housing market prices. Thus, the development of professional rental business, including companies with state participation, may become a promising direction in the new institutional environment. After researching factors that limit the development of the professional rental housing and evaluation of the influence of rental business on the housing markets, the authors make some proposals for stimulating rental segment of the market. Development of the professional rental business requires solving such scientific problems in the sphere of evaluation of housing development projects and financial management of economic entities’ as: theoretical substantiation of approaches to form and evaluate rental housing value, development of methodological tools and practical recommendations on value management of rental housing projects portfolio.

Influence of Neighborhood Factors on Sleep in Home Dwelling Multiethnic Older Adults With Dementia

Current scientific paradigms inadequately capture complex clinical, behavioral, and sociocultural factors impacting health and well-being in persons living with dementia (PLWD). The purpose of this study was to identify differences in individual and neighborhood-level factors contributing to sleep among multi-ethnic PLWD. Wrist actigraphy measured objective sleep characteristics. Subjective sleep was assessed using the PROMIS sleep measure. GIS mapping analyzed neighborhood-level factors (walkability, green space, crime index, density). Walkability was significantly associated with subjective sleep (p.006) controlling for age and dementia stage. Number of night awakenings was significantly associated with density, crime and housing value (p<.001). PLWD in neighborhoods with higher population density, annual crime, low median home and low walkability would benefit from interventions targeting unsupportive neighborhood environments to improve sleep.