Regression Case

(e) Is there a significant relationship between the selling price and the assessed value of the house? Use 5 % level of significance.

Obtain a scatterplot of the sales on the vertical axis against comtype on the horizontal axis. This will give you a good idea of whether different categories of comtype appear to differ in sales. In the scatterplot, you should see that sales in the middle categories 3 - 6 are in similar ranges on the vertical axis, but 1 and 2 have somewhat higher sales, and category 7 appears to have somewhat lower sales. This implies that, when you create dummy variables for comtype, dummy variables for categories 1, 2, 7 are likely to be statistically significant in the multiple regression model (and dummy variables for categories 3 - 6 are likely to be not significant). Although it would be desirable to also obtain the scatterplot of sales against every other X variable, you can omit these if you do not have time, and use the correlation coefficients instead (see step 4 below).

Pam and Susan’s department stores are in the process of opening a new business unit. There are two locations that are being considered for the new store and decision is based upon estimates of sales for both of them. My job is to use data gathered from each store as well census data in store’s trading zones to predict sales at both of the sites that are being consider for their newest store.

Making yourself aware of the neighborhood and its growth, studying when the market peeks or if it is still growing, and studying the areas general financial foundation of the city, are all important things you need to be aware of when buying a house. According to Mankiw, "In any market, buyers look at the price when determining how much to demand, and sellers look at the price when deciding how much to supply. As a result of the decisions that buyers and sellers make, market prices reflect both the value of a good to society and the cost to society of making the good." This is one of the principles of economics that can quickly affect the profit of this investment.

There are several factors that can influence the housing industry economically. Supply and demand coupled with price elasticity can affect the housing industry. Negative and positive externalities, wage inequality, and the monetary and fiscal policies can all have substantial affect the industry of new homes. It must also be determined exactly how the economy affects the industry in both positive and negative ways.

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?

The decision variable for the test carried out on the attributes measured revealed that the best combination of variables would be one characterized by building size and lot size for maximum number of customers. The building size having a value of 12.74 units with customer lot size at 1.7 units.

Since the maximum value of the predictor variable (calls) is used to formulate the given regression model is 201.00, which is less than 300, we cannot use the given regression model to accurately estimate the weekly sales for weekly call of 300. So we can’t say anything about the weekly sales when weekly calls are 300.

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.

To calculate the mean I added up the sum of days to sell and divided by 18. The mean is 203,188.89, which mean the average of the listed price. The median was calculated by listing the numbers in numerical order from lowest to highest and located the number in the middle of 203,500. The median represents the middle number of the days that it took to sell the condominiums. After calculating the median I located the minimum and maximum based the lowest and highest data, which are 135,500 and 292,500. These represent the range of the sale price. Lastly, I used the formula to get the standard deviation of 43,891.72, which measures the variability.

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.

The data for the second test to be conducted by our group consists of lot sizes of the residential properties that are up for sale in Toronto and Vancouver. The samples are represented in m2 (metres squared; area of the land in which the residential properties are built on). The data taken are based on the properties that are up for

The difference in asking and selling price could be correlated with the number of days on the market and very similar reasoning as to why it is a weak variable. The seller will most likely not allow much difference in their asking and selling price because of the appraised value. Also, looking at the coefficients of these two variables, I can see that change in them do not impact the price very much. The number of bedrooms is not a significant characteristic because it is correlated with the square footage. It seems a little odd that the number of garages is insignificant. However, the mean number of garages for this data is above one, meaning the average house in Blowing Rock has at least one garage. With a garage being fairly standard amenity for homes in Blowing Rock I can understand it not being a very significant factor on the price compared to the other characteristics. Living in a subdivision is not significant for this town as well.

Run the regression Report your answer in the format of equation 5.8 (Chapter 5, p. 152) in the textbook including and the standard error of the regression (SER). Interpret the estimated slope parameter for LOT. In the interpretation, please note that PRICE is measured in thousands of dollars and LOT is measured in acres.