Running head: REAL ESTATE FINAL PROJECT ! ! ! ! ! ! ! Real Estate Final Project Elodie Auvray Ottawa University ! ! ! ! ! ! ! ! ! ! !1 REAL ESTATE FINAL PROJECT !2 Abstract A home is probably one of the few investments most students have to encounter on their college journey. After finding, applying and being accepted in an university or college. The distance, location and other factors influence the prices of theses houses. Student are struggling to find affordable living near their campus. During this course, I learned how to use statistical methods. This project will explore the relationship between variables and selling prices in the real estate. The analyzed data will help reaching a conclusion in accordance with the hypotheses. I will be analyzing the real estate spreadsheet given for this assignment to figure out if the price of the homes near the schools are more expensive than the homes not near the schools. ! ! ! ! ! ! ! ! ! ! ! ! REAL ESTATE FINAL PROJECT !3 Real Estate Final Project Introduction Real Estate is very interesting and it becomes really interesting since factors usually dictates pricing. According to Jackie Wang (2015), \"students struggle to find affordable housing options near campus\". Few programs had been created in Austin such as \"S.M.A.R.T. housing\" (Jackie Wang, 2015). Perhaps while these housing are more affordable, others remain expensive. Problem Statement and Research Question It has become a challenge for students to find affordable housing. It might be because of different factors or simply because of the location. These housing are such a great deal for students that the demands are overflowing. You can observe on Figure 1 that The Villas on 26th which is the closest one from the campus will be soon taken rapidly. That leaves the students looking for apartments outside the program \"S.M.A.R.T. housing\" (Jackie Wang, 2015). Figure 1. The West Campus Apartment Complex (The Daily Texan). REAL ESTATE FINAL PROJECT !4 Through my investigation I will be answering to this question: does the data suggest that homes under $200 are more likely than homes over $200 to be closer to schools. Research Approach Selection of Variables for Study The response variable to the research question leads to classify whether a home is likely to be over $200 if closer to school or not. In our case, we are dealing with an explanatory grouping variable such as the distance. Data and Variables under Consideration I listed the independent and dependent variable below. Dependent Variables Home Prices : The weekly home prices are the independent variable of this study. The prices are in the real estate excel spreadsheet. Independent Variables : The location, bedrooms, pools, distance, township, garage and baths are the independent variable of this study. These variables are in the real estate excel spreadsheet. 1. Baths: the amount of baths varies from 1.5 to 3. 2. Bedrooms: the amount of bedrooms varies from 2 to 8. 3. Pools: the amount of pools varies from 0 to 1. 4. Distance: the amount of distances varies from 1 to 25 miles. 5. Township: the amount of townships varies from 1 to 5. 6. Garage: the amount of garages varies from 0 to 1. Sources of Data REAL ESTATE FINAL PROJECT !5 The primary source of data used for this statistical analysis is the following: a) Real Estate Data Spreadsheet of 105 Austin homes attached to this report. Statistical Analysis Statistical Tools Used The following statistical tools are used in this project: Frequency Distribution Tables Descriptive statistics Hypothesis Testing Price Frequency 0-100 0 100-200 42 200-300 56 300-400 7 TOTAL 105 Figure 2. Frequency Distribution Table 0-100 100-200 200-300 300-400 7% 40% 53% REAL ESTATE FINAL PROJECT !6 Figure 3. Pie-Chart of Home Prices based on Frequency Distribution Table Note: 42 out of 105 homes or 40% are homes sold under $200. While 63 out of 105 homes or 60% are homes sold over $200. PRICE BEDROOMS SIZE POOL DISTANCE TWNSHIP GARAGE BATHS 221.10 3.8 2223.81 0.36 14.63 3.10 0.68 2.08 MEDIAN 213.6 4 2200 0 15 3 1 2 MODE 188.3 4 2100 0 16 4 1 2 STANDARD DEVIATION 47.11 1.50 248.65 0.4828 4.8739 1.2854 0.4701 0.3929 125 2 1600 0 6 1 0 1.5 345.3 8 2900 1 28 5 1 3 23215.8 399 233500 38 1536 326 71 218.5 105 105 105 105 105 105 105 105 MEAN MINIMUM MAXIMUM SUM COUNT Figure 4. Descriptive statistics Homes under $200 A. Mean=176.87 B. Standard Deviation=18 C. Number=42/105 Homes over $200 A. Mean=250.59 B. Standard Deviation=36.04 C. Number=63/105 Note: The difference in the mean selling price of homes under $200 and homes over $200 is $250.59-$176.87=$73.72 ! REAL ESTATE FINAL PROJECT !7 ! Hypothesis According to Lind, Marchal, W. G., & Wathen (2015), a hypothesis is \"a statement about a population parameter subjective to verification\". I will be using the null and alternative statements related to the research question. I will be using the p-value. Null hypothesis Definition: a statement about the value of a population parameter developed for the purpose of testing numerical evidence. Ho: There is no distance effect regarding the price of homes P value is large. Alternative hypothesis Definition: a statement that is accepted if the sample data provide sufficient evidence that the null hypothesis is false. H1: There is a distance effect regarding the price of homes. P value is small. Note: Ho: There is no distance effect regarding the price of homes H1: There is a distance effect regarding the price of homes. Method For my study, I will be using the t-test. I will be able to compare the groups of data in terms of location. Through this comparison, I should be able to to find any significant different between them. REAL ESTATE FINAL PROJECT !8 Note: If the p-value in a t-test is small, then the alternative hypotheses is the answer. And if the ttest is large, then the null hypotheses is the answer. Graphs of Homes Over and Under $200 ! ! distance 30 distance 22.5 22 16.5 15 11 7.5 5.5 0 0 125 155.4 172.4 175 179 186.7 188.3 192.6 199 205.1 209.3 216.8 224 233 243.7 251.4 263.2 273.2 294.3 326.3 Homes Under $200 Homes Over $200 ! Distance Data Homes Under $200 18 28 28 12 13 16 19 16 19 16 23 16 21 14 9 11 24 15 17 10 10 8 11 19 14 21 26 8 15 20 14 11 24 18 16 14 14 11 19 10 18 19 16 15 9 17 14 13 8 11 7 9 16 16 21 ! Distance Data Homes Over $200 20 9 11 21 10 8 15 20 13 19 16 11 13 8 15 21 9 7 13 11 15 9 19 15 12 18 18 9 6 17 8 20 17 ! ! ! 13 12 7 9 12 7 18 14 REAL ESTATE FINAL PROJECT !9 Descriptive Statistics Homes Over and Under $200 MEAN OBSERVATIONS STANDARD DEVIATION Homes under $200 Homes over $200 16.31 13.51 42 63 5.22 4.32 P (t<=t) one-tail 0.5128 Conclusion, Implication, and Recommendations The t-test gave a p-value of 0.5128. This is a small p-value. As a result, there is no significant difference between prices according to the distance from the campus. About half of the homes are over $200, only 63 out of 105. As a result, the prices of the homes are not dictated by the distance from the campus. Also the difference in the mean selling price of homes under $200 and homes over $200 is $73.72 which is not much. I observe a nonconstant pattern with the prices according to the location. I would tell the students to be aware of that and research homes near the campus, they might find an affordable home closer than they think. ! ! ! ! ! ! ! ! REAL ESTATE FINAL PROJECT !10 References Lind, D. A., Marchal, W. G., & Wathen, S. A. (2015). Basic Statistics for Business and Economics (16th ed.). New York, NY: McGraw-Hill Irwin. Wang, J..(2015, November 13). Students struggle to find affordable housing options near campus. Retrieved from http://www.dailytexanonline.com/2015/11/13/students-struggleto-find-affordable-housing-options-near-campus