Description
Use the data and analysis in those 3 assignments to create a 6 to 8 mins long ppt and ppt scripts, 2 examples are provided for reference.
Executive Summary
This report sought to achieve three primary research areas: Time series analysis on the total dollar amount of residential real estate sales in South Jamaica, a multiple regression to come up with another forecast for the next 8 quarters, and a multiple regression model to determine the sale of a given residential property in the neighborhood.
R functions were used to forecast sales for the next 2 years. In the next 8 quarters, sales are predicted to peak in the second quarters(Q2) and be at an all-time low in the fourth quarters(Q4). This is in line with sales of previous years. The biggest bargains were the 1,290 square feet property on 76 Hewitt Avenue that went for 50,000 sale units. The most overpriced property in South Jamaica was the 2,992 square feet property on 158 Douglas Road that went for 2,700.000 sale units. This is due to the location of the property, and the type of building.
There was no evidence of multicollinearity in the regression model. We therefore concluded that no redundant variable was present. It was determined that gross square feet was the most useful predictor of the sales price.
Findings
Time Series analysis on sales
We conduct a time series analysis on residential real estate sales in South Jamaica between 2009 and 2016. According to the historical data on residential estate sales in the South Jamaica neighborhood, the quarterly sales were rising in cycles. These changes could be attributed to falling mortgage rates, booming tourism, and loose credit environment.
Quarter sales indicate that sales have a tendency of peaking at Q2 and bottoming at Q4. Q3 and Q1 show average sales in comparison to the rest. The shadow represents 80% to 95% forecasts of where the sales will range in the next two years.
Multiple Regression Model for Forecast
With a multiple of 0.09794, we see that time series is responsible for 9.80% of the regression. This means that it is not at all statistically significant.
The regression model shows that the next two years will follow the same pattern as before. Sales will greatly drop in the fourth quarter and experience a boost in the second quarter. This could be because most people spend the latter part of the year visiting family or travelling, and not many think to shop for a home. The second quarter, however, is prime home shopping time, especially because of the favorable weather. The real estate companies need to consider giving potential buyers incentives to encourage them to purchase homes.
Multiple Regression Model for Property Price Analysis
We find that the p-value of the F-statistic is 1.379 e-8, which is statistically significant. This means that, at least, one of the predictor variables is significantly related to the outcome variable. With a t statistic p-value of 4.07e-0.8, gross square feet is significantly associated with changes in sales. Holding all other values constant, an additional 1,000 gross square feet leads to an increase in sales by approximately 0.019*1000 = 19 sale units. We can therefore conclude that gross square feet is the most useful predictor of the amount of a sale of property in South Jamaica.
The least useful predictors of the amount of a sale would be the year built. All other factors held constant, an additional 1,000 years would lead to a decrease of only 0.004137*1000 = 4.137 sale units.
The adjusted = 0.4748, meaning that 47% of the variance in the measure of sales can be accounted for by the variables.
We test for multicollinearity using the Variance Inflation Factor (VIF), where;
We find that VIF= 1.90. This shows little or no evidence of multicollinearity of the sales price with other explanatory variables. Hence, there are no redundant independent variables in the regression model.
The biggest bargains were the 1,290 square feet property on 76 Hewitt Avenue that went for 50,000 sale units. The most overpriced property in South Jamaica was the 2,992 square feet property on 158 Douglas Road that went for 2,700.000 sale units. This could be because the properties on Douglas Road are mostly of a higher building class. Hence the location itself could be appealing to buyers and future property owners.
Real estate companies need to focus on ways to boost sales in the fourth quarter. They could sell these houses as vacation homes or sell them off to renters who could turn them into Airbnb’s. They could also offer discounts and other financial incentives to attract buyers.
Appendix
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