Real Estate Data Set

First use the STEPWISE technique (chapter 15) to determine which combination of Independent variables should be used to predict the price of a house. It might not be all independent variables are good predictors.

17. St. Augustine Company is trying to decide which one of two contracts it will accept. The costs and revenues associated with

Provide histograms and CDF of prices. Compare estimated prices to realized prices using stochastic dominance and mean-variance criteria.

Because of the method of monthly data collection, absolute randomness could not be obtained; however, it was decided that 5 iterations was sufficient because the sixth iteration showed a decrease in the quality of the residual plots. The first test performed was the p-value test of the individual variables. A p-value is the probability, ranging from 0 to 1, of obtaining a test statistic similar to the one that was actually observed. The only input that did not have a p-value less than 0.05, which was the chosen significance level, was the “Number of Walmarts” variable; the number of Walmarts has no specific effect on the output, property crime rate. The R2 of the analysis, or the coefficient of determination, provides a measure of how well future outcomes are likely to be predicted by the model. R2 values range from 0 to 100% (or 0 and 1) and the

The real estate division was estimated to have a fair value of $13,890,000. This was determined by totaling the number of lots expected to sell within the next four years and multiplying it by the price per lot of $180,000. After determining total lot sales, a 20% discount rate was applied as suggested by current market conditions. Given the unique nature of the real estate development, it is not believed that there are any comparable developments to find a market multiple.

some initial work estimating the likely relationship between what tenants would be willing to pay

The business literature involving human capital shows that education influences an individual’s annual income. Combined, these may influence family size. With this in mind, what should the real estate builder be particularly concerned with when analyzing the multiple regression model?

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.

This case investigates the factors that are affecting the sale price of Oceanside condominium units. The relationship between these factors and sale price has remained the same despite condo sale prices increasing drastically over the past 20 years.

Correct valuation of real assets can present challenges to financial analysts. Different models can be used to arrive at the closest estimate of value and yet certain issues will always arise.

Automated valuation models (AVM) according to the RICS AVM Standards working group are systems that use one or more mathematical techniques to provide an estimate of the value of a specified property at a specified date, accompanied by a measure of confidence in the accuracy of the result, without human intervention post-initiation. They combine property sales data, property attributes data as well as local market information (RICS 2013, Corelogic (n.d)); these form the variables that are fed into the model. Models typically comprise one dependent variable which is the estimated property value and several independent variables (property attributes data) which take turns in explaining the dependent variable (RICS, 2013). AVMs vary depending on the modelling technique adopted, the methodology and independent variables adopted. Choice is solely down to the provider’s specification (RICS, 2013). Examples of the different models include; multiple regression model, indexation, sales comparison models and automated comparable selection and artificial neural networks. AVMs have been around for a while. However, market acceptance has been slow, tentative and somewhat phased.

Real Estate has been a driving force in world economies since the days of Babylon, one of the most fantastic developments the world has ever known, and the desire to create, not destroy, is alive and well. As the world grows more populous and available land diminishes, the opportunities that real estate development has to offer are vast and obtainable. As we descend from the booming 90s and find ourselves burdened with the realities of our own self-inflicted economic implosion caused by greed and speculation, many investors find solace in the tangible world of real estate. Moreover, the destructive effects of September the 11th are still disgorged down through our economy, which is causing an ever-increasing desire for a more concrete wealth-building option not offered by many paper investments. Many investors are still coping with the fall of such giants as Enron and world to come, while others are looking for the stability that the real estate market offers, and when you add all these ingredients to the grossly over-priced US stock market, one find a more appealing meal in the fascinating world of real estate.

As mentioned in class, one commonly employed solution to heteroscedasticity is to adjust the standard errors for the possible presence of heteroskedasticity, i.e. we compute the heteroskedasticity-robust standard errors, which are also referred to as heteroskedasticity-consistent standard errors. Rerun the regression in part (2) with the OLS standard errors replaced by heteroskedasticity-robust standard errors. Comment on the differences between the OLS standard errors in part (2) and the heteroskedasticity-robust standard errors in this part.

In this study, the model with two estimated threshold parameters, γ1 and γ2, is chosen because of the heterogeneity of estate market and financial market in different cities. So, the model is following:

This research will be made on articles concerning how to provide a detailed overview of the selected U.S. investment indicating the rationale for Real Estate Investment; we will select five financial ratios, then we will analyze the past three years of financial data for Real Estate Investment. Therefore, we are going to analyze the price of the Real Estate investment to its market index for the past five years. In this paper, we will create a trend line that depicts the price movement for the investment against the market index movement using elements of Microsoft Office, such as Excel. Then, finally we will determine the type of person who would be the best candidate for the chosen investment (e.g., the risk adverse investor, an aggressive investor, etc.); furthermore, we are going to provide a rationale for why this investment is a solid one, and support the assertion that someone should invest in this stock. Therefore, to conclude this paper, we are going to develop effective Real Estate Investment strategy to address the challenges and issues.