Próximo SlideShare
Student 26 Revised Assign.1
Cargando en ... 3
1 de 12

### Determinants Of Residential Property Value In Las Vegas

1. DETERMINANTS OF RESIDENTIAL PROPERTY VALUE IN LAS VEGAS An analysis of the determinants of Las Vegas housing market. Jared Bilberry
2. Introduction The Las Vegas housing market is as unpredictable as the slot machines within the city itself. In In the early 2000’s, Las Vegas faced a major housing bubble due to multiple factors - demand, speculation, and exuberant spending to name a few. What once was a little big town with casinos, truly became a world class city, one that would rival nearby metropolises such as Los Angeles, Seattle, and San Francisco. In 2021 and beyond, Vegas has a new set of parameters that determine the overall housing market. I put together a table to describe these relationships between prices and the explanatory variables within a given data set. First, I ran the data given consisting of said variables (sqft, bed, bath, pool) through Stata to explain the rationale for the expected signs (+/-/n/a). Once provided, I then constructed a second table to describe the summary statistics (This would include common terms such as mean, standard deviation, and min. and max. values for all variables). From there, I ran a matrix correlation that demonstrates the predictor variables correlate with the target variable (determinants of the Las Vegas housing market.) I put together and displayed scatter plots that show the correlation between each of the predictor variables and determinants (dependent variables) that will be analyzed. Finally, I constructed two separate regression tables displaying the full and reduced model with average determinants. With this information, we ultimately determine the slope p-value approach to test the statistical significance of each of the slope coefficients, interpretations, and test for the overall significance of the model.
3. Model This model that is used is a function of all predictor variables, since all of them are expected to influence the determinants of the Las Vegas housing market. Determinants = f (sqft, bed, bath, pool) Table 1: Variables, definitions and expected signs Variables Definitions Expected Sign Price Sale price of home + SQFT Square feet, measuring the size of living space + BED Number of bedrooms + BATH Number of bathrooms + POOL 1 if the house has a pool or spa and 0 otherwise + Table 1 displays a predictor of all variables. In this case, I assumed that every variable would have a positive impact on the buyer. This may be based off the prospective buyer’s attitude towards something like a pool, which would include more maintenance, but also allows them to sell the property after a few years at a higher price to family who may want a pool. Other variables such as bed, bath and sq ft may also be positives, as many people would prefer more for less. For example, more bathrooms may not be a benefit for the average single person, but it usually wouldn’t be a negative (-) as it doesn’t hurt the property value. Overall, I decided to leave every sign positive (+) due to the mentality that most variables shouldn’t hurt the buyer in the long run, specifically when to comes to property and re-sale values.
4. Table (2) Descriptive results The average price is equal to \$192600.10, and the table shows as descriptive statistics for all the variables. As we can see, average price of the home in the data set is \$192,600.10 with a standard deviation equal to \$90,684.38. The cheapest price is \$71,000, and the price of the most expensive house in the data set is \$674,500. Also, we can see that the Summerlin, Green Valley, Northwest, and East Las Vegas areas all have close to the same percentage of houses of .26, .30, .24, and .21 for the study. I also analyzed bed, bath, pool and sqft while following the same model for price. With the other variables in the descriptive results, the data demonstrates the trends for other variables within the results.
5. Table (3) The matrix of correlation coefficients The matrix of correlation coefficient reveals that a strong correlation between (+.71) the size of the house and the price. Also, the number of beds show a weak positive correlation (+.24) between the number of beds and the price of the house. The number of bathrooms showed a moderate positive correlation with the price of the house. Summerlin shows the highest positive correlation between the price and the house, comparing to Green Valley and especially East and Northwest Las Vegas, which have negative correlations. As stated prior, the data depends on the year used and what economic market it represents.
6. Figure (1) Scatter plot of PRICE and SQFT The scatterplot shown between the price and sq ft of house shows a positive correlation where an increasing the number sq ft in the house will increase the price.
7. Figure (2) Scatter plot of PRICE and BED The scatter plot between price and the number of beds shows a slightly positive correlation that is associated with what we found in correlation matrix, which was expected at the beginning of the project.
8. Figure (3) Scatter plot of PRICE and BATH This scatter plot also reflects a slight positive correlation – as the number of baths increases, prices increase as well. Ultimately, this reflects (generally speaking) what I stated in Table 1. Empirical Results Table 4 displays the results of a Full and Reduced regression model. We then test the statistical significance of the slope coefficients at the .1, .05, and .01, respectively.
9. Pricef=12385.19 +85.07sqft - 15884.94bed+10277.4bath+10058.48pool+57413.72summerlin+24723.08greenvalley- 5745.75east Pricer=32331.87 +88.16sqft -23975.04bed+10277.4bath+9732.33pool Table 1: Regression results representing the Full and Reduced models. Parentheses indicate p- values, * p<0.1, ** p<0.05, *** p<0.01 Variables Full Model Reduced Model Constant 12385.19 32331.87 -0.65 -0.209 sqft 85.07 88.16 (0.000) *** (0.000)*** bed -15884.94 -23975.04 (0.018)** (0.001)*** bath 10277.4 19248.88 -0.481 -0.213 pool 10058.48 9732.33 -0.4 -0.448 Summerlin 57413.72 (0.000)*** Green valley 24723.08 (0.054)* East -5745.75 -0.681 N 164 164 R2 0.6141 0.5455 F Fc (7,156) = 35.47 Fc (4,159) = 47.71
10. We can see that the sq ft is a positive significant correlation which tells us that increasing the house with 1 unit of a sq ft will increase the price by \$85, on average. This keeps all other variables constant. Bed is negative and significant at .05 and .10, but in the reduced model, it’s strongly significant at .01. Bath is positive, but not significant in both full and reduced model. Pool is positive and not significant in both models. The variable Summer was strongly and positively significant at .01. Green Valley is significant as well, but not as strong as Summerlin. Which obviously means that buyers overwhelmingly prefer Summerlin to Green valley in the given data set. On the other hand, the East side of the valley has a negative coefficient with no significance, even at .10. In other the words, the prices in the East Las Vegas will not be affected in this data set. Conclusion Even with the lingering Covid-19 pandemic, the Las Vegas real estate market is red hot. While many businesses are still struggling with how to get back their labor force after abrupt layoffs, we can see the data shows that people still like the “bells and whistles” when it comes to purchasing a house. According to the statistical information, Summerlin was and is still an expensive market for most, and East Las Vegas was significantly less popular. Supply and demand are two factors that will always play a role in real estate appreciation, and location is one of the biggest factors in this equation. Other data shows that extra add-ons such as beds, bath, and pools don’t really affect the overall price of the house in a way that many would expect. Overall, the study reflects what many in the Las Vegas valley already know: people want to live in good neighborhoods, with good amenities and ultimately be able to sell their properties at a reasonable profit.
11. Appendix 1: Full Model Regression Results
12. Appendix 2: Reduced Model Regression Results