Evaluation Of Hedonic Price Model

In line with previous research, RSOs are located in in economically and socially depressed areas (Clark & Duwe, 2015; Gordon, 2013; Hipp, Turner, & Janetta, 2010; Levenson & Cotter, 2005; Mustaine & Tewksbury, 2011; Socia, 2013a, 2013b, 2014; Suresh, Mustaine, Tewksbury, & Higgins, 2010; Tewksbury, 2002, 2007; Tewksbury, Jennings, & Zgoba, 2012; Tewksbury & Mustaine, 2006). There is also the potential of concentrations of RSOs to further push the area into a more depressed area, socially and economically (Mustaine & Tewksbury, 2011; Zevitz, 2003). Property and neighborhood are expected to go into their traditional patterns. For instance, home sale values are lower when the sold property has no AC, less parcel acreage, greater number of years, less basement and building square feet, and no fireplaces. In terms of neighborhood characteristics, it is expected that home sale values are to be decreased in areas with higher rates of Black residents, Hispanics, female-headed households, and lesser rates of Bachelor-degree

The amount of income spent on housing is an important component of the cost of living. The total costs of housing for homeowners might include mortgage payments, property taxes, and utility costs (water, heat, electricity). An economist selected a sample

Inclusionary zoning is a tool to help accomplish affordable housing. The main goal of it is to build a certain percentages of house but to where the houses can be afforded by middle/low income earners. Incentives are provided to act as cost offsets and include density bonuses, tax abatement, and reduced parking charges. Density bonuses motivate developers to have additional units which result in an increased profit margin. On subscription to the inclusionary zoning, benefits accrued eventually lower costs that would have otherwise been charged on the developers. This technique provides a sustainable method of dealing with housing shortage (Dietderich 13). The municipality, however, cannot force developers to set a particular minimum price and therefore the government can only provide incentives to developers to voluntarily provide the units for low-income earners.

Many large cities are experiencing skyrocketing rental/housing price because of the excessive demand and more importantly, the scarcity of land. San Francisco, as one of the most important financial centers in the world, is facing a serious dilemma due to its inflated rental/housing prices; people’s dissatisfaction over the government grow as they are excluded from the access to proper housing. San Francisco has the highest rents of any large city in the United States, which makes people, especially from low and medium income group, hard to acquire housing near their workplaces and force to commute from other regions of Bay area into the city. In other words, the high housing prices are pushing people as well as the productive industry out of the city. While people are accusing the government of being responsible for the failure of housing since it underestimates the rate of population growth, which leads to a shortage in the housing market. However, statistic shows that San Francisco actually built more than 30 percent more housing units per capita than New York City and Los Angeles in 2014. (Henry Grabar, 2015) It is because such shortage is artificially created by the policy makers to constraint land use, hence, increasing the supply of housing does not efficiently decrease the housing. The fundamental cause of this imbalanced housing market is the poor allocation of land use.

Supply and demand play a major role in the value of real estate. The forces behind supply and demand include physical, economic, political, sociological, and location issues. The location of the subject property within St. Johns provides many positives with respect to value. First, the proximity to local highways not only provides for ingress and egress, but gives the subject neighborhood exposure to potential tenants or clients. It provides a quick means of transportation for employees or customers. Second, the location of St. Johns in proximity to Lansing and the rest of Michigan is an advantage. This proximity provides additional workforce, complementary businesses, and suppliers.

Macroeconomics is an excellent tool for the analysis of the housing industry as something like a capital good, as a home is considered to be, cannot easily be studied in a short-term platform. Real estate is a good that costs several times more than an average persons annual income, in the United States that number is typically 7 times as much, and in the United Kingdom that number is 14 times as much. Several factors of both supply and demand directly impact the housing market on a macroeconomic scale. (Business Economics, 1)

This case involves an investigation of the factors that affect the sale price of Oceanside condominium units. It represents an extension of an analysis of the same data by Herman Kelting (1979). Although condo sale prices have increased dramatically over the past 20 years, the relationship between these factors and sale price remain about the same. Consequently, the data provide valuable insight into today’s condominium sales market.

Table 2 presents estimation results of our empirical model represented by equation (5). The first column shows estimation results only including housing prices and traffic congestion index without other control variables. The result presents that traffic congestion growth is negatively associated with growth in median housing prices. Such results are slightly changed when including control variables or fixed-effects terms, but almost consistent and robust as shown in the model (2) through model (6). Since different locations and different time influence growth in housing prices (see Figure 5, again), model (6) that include locational and time fixed-effects is the best fit to explain the relationship between congestion growth and housing price

Housing demand includes household growth, real incomes, real wealth, tax concessions to both owner-occupied and rental housing, concessions to first homebuyers, returns on alternative investments, cost and availability of finance for housing and the institutional structure affecting housing finance provision (Yates, 2008). The growth in the number of households and in real income results in the increased pressure on housing demand.

Multiple reasons exist for the the lack of affordable housing. On the demand side these include population growth and increased migration to urban areas, easily accessible housing finance, tax incentives and a “strong cultural preference for owner-occupied detached houses”. On the supply side, affordability problems are exacerbated by inflexible and slow responses to the need for new housing stock, lack of infrastructure and generally inefficient planning processes and development assessment by local governments.

Considering the mean of the log house prices in a postcode as the key price variable, with another crucial measure of the housing market; the log of the number of sales in a postcode in the given year. Correlating the suburbs data by the distance between their centroid with the help of FindMap Pty Ltd. was done (Davidoff & Leigh, 2013).

This research topic is significant to the current property market in Singapore and its sudden increased demand for houses despite the economic downturn, exploring deeper as to whether the government policies were the real influential causes to this boom in property demand. It has relevance to the economic concepts of demand and supply, elasticity, inflation and monopolistic competition. This topic is worthy of investigation because it is a hot media topic in Singapore, and is widely debated in the country because it’s the most expensive household asset.[2]

In this report, the question “How much of the changes in the median selling price of homes in a city can be explained by the changes in median income of that city?” is answered. Home ownership is an important aspect of one’s life stages, and home prices are determined by demand and supply. The demand curve is affected by the one’s income, such that as one’s income increases, one is more willing to pay a higher price for the same quantity of goods (Baye & Prince, 2014). However, there are many other factors that might affect the demand curve, e.g. no. of children, in the household, the perceived quality of education in the school district, or the number of job positions (filled or open) around the city. According to Burda

A difficult characteristic to understand about the housing market is how a price is given for a particular house. That price will be designated to that particular house alone. All houses have various pricing, so I can’t always assume that one will cost more or less than any other. The pricing for houses vary based on their characteristics. Each characteristic must be analyzed to determine its contribution or detraction toward the price. I have taken some of these characteristics and modeled the relationship between them and the price of real estate for a specific area.

In spatial analysis, the aim is often to identify the natural relationship between pairs of variables. And the most common type of analysis used to achieve this aim is regression (Fotheringham & Rogerson, 2009, p. 243). In a conventional linear regression model, we use a single equation to assess the overall relationships between a single dependent and more independent variables across space. An important assumption underlying this approach is that the relationships of interest are stationary or homogeneous over space (Fotheringham & Charlton, 1998). So, the parameter estimates from the regression model are constant over space (Fotheringham & Rogerson, 2009, p. 244). Relationships which exhibit spatial nonstationarity (or heterogeneity) will create the problem for the interpretation of parameter estimates from a linear regression model (Fotheringham & Charlton, 1998). To address this issue, Dr. Brunsdaon et al., (1996) developed a