...Statistics and Business Analytics, FALL 2014 ES-1, Grp 7: Koel BANERJEE, Sahil BIHARI, Rong TIAN, Jean-Baptiste VARNIER, Dmitry YATSENKO House Prices Analysis in the DFW Area Report prepared for M. Sam Horton HEC PARIS MBA PROGRAM FALL 2014 FINAL TEAM PROJECT 0 Statistics and Business Analytics, FALL 2014 ES-1, Grp 7: Koel BANERJEE, Sahil BIHARI, Rong TIAN, Jean-Baptiste VARNIER, Dmitry YATSENKO This report summarizes the statistical analysis performed in relation to the sale of houses in the Dallas Fort Worth (DFW) area, which includes Dallas, Fort Worth, Arlington and the MidCities. This report was prepared exclusively for M. Horton. The purpose of the statistical analysis is to investigate whether the claims from two former Horton Realty customers that their houses were underpriced are justified. 1) Descriptive analysis In preparation of this report, we analyzed residential sales data received from Pat McCloskey that occurred in 2010 in the DFW area. The sample data received included: Categorical variables: the sale quarter (ordinal), the location of the house within the DFW metroplex (nominal), the real estate agency that sold the home (nominal), the observation ID (nominal) and, Quantitative variables: the sale price (ratio), the size of the home (ratio), the number of bedrooms (ratio) and the age of the house (ratio). A descriptive analysis of our sample is performed in Sections 1.1 and 1.2. The observations made in these two sections concern the sample...
Words: 5462 - Pages: 22
...different in Shanghai and Chongqing, varying in tax rate and charging targets. Property tax in Shanghai aims at newly-bought houses, while that of Chongqing aims at high-class apartments. However, they both impose restrictions on non-local residents and their tax rates are both pretty low (See Exhibit 1). Compared with property tax policy of other countries, China’s tax trial is far from mature. Its tax rate stays around 1%, while Hong Kong is 17% and Japan is 10%. Besides, the current trial policy only covers a small proportion of the whole house market. Goals of the Property Tax Trial The short-term goal of the property tax trial is to depress the rapid growth of housing prices through two paths. First, by increasing the holding cost of investment houses and prolonging the returning period, property tax would put financial pressure on real-estate speculators and therefore depress the demand, which would decrease housing prices. Second, sales of investment houses would also increase the supply of the real-estate market, which would also decrease housing prices. The ultimate goal of the property tax trial is to change the fiscal mode of local governments. Currently, local governments are mainly relying on the sales of land to make ends meet, which is often called the Land Fiscal Mode. This provides motivations for the local governments to push up the housing prices. With the introduction of the property tax, local governments will be able to get...
Words: 2084 - Pages: 9
...the age of the home make a difference in the price? What about the square feet of the home? Does having a house with more square feet mean that the price of the home will increase? Well my fiancé and I are looking to buy a home and have come to the conclusion of five factors that we think are the most important. Using statistics, we will narrow our search down from 108 homes to only a few homes. The first thing, we need to discuss is the dependent and independent variables. Since we are most concerned with the price of the home and how other factors affect it, we will use PRICE as the dependent variable. We have other factors that influence the price such as square feet (home size), number of bedrooms, number of bathrooms, heat (gas forced or electric), style (ranch, two floor, or tri-level), garage, basement, fireplace, age of the home, and the school district which will be our independent variables. Out of these, we have decided to pay close attention to the square feet (home size of 1900 square feet or more), number of bathrooms (3 or more), heat (gas forced), basement, and the age of the home (less than or equal to 10 years). We chose these factors because we wanted to know what kind of relationship, if any, they have with the price of the home. To figure that out, we will be doing a series of tests to discuss the price difference for the independent variables. Then we will get the probability and confidence interval for price in relation to our criteria which will lead...
Words: 2412 - Pages: 10
...Description The price of the varying houses ranged from $71,000 to $131,000 with a mean of $100,146.32. The standard error of the price of the homes was $13,298.79. These measurements were measured while observing 150 different homes throughout the course of the study. Several variables were thought to affect the price of a home; these included the location, size of the house, size of the lot, size of the garage, age of the house, number of bedrooms and bathrooms, the number of floors in the house, and exterior siding of the house. The size of a house directly corresponds to the price of a home. In the sample population the houses ranged from 945 to 3202 square feet with a mean of 1581.82 square feet and a standard error of 29.33599 measured in feet squared. Also, the lot size of each house was taken into account for the model. The population’s lot size had a mean and standard error of 303.8133 square feet and 145.9674 square feet respectively. This was a direct result of the range of data—0 to 701 square feet. Another important selling point in a house is the number of rooms specifically bedrooms and bathrooms. In the sample population a mean number of bedrooms were found to be 3.06 with a standard error of .041483 on a range of 2 to 5 bedrooms. In terms of bathrooms, the average amount of bathrooms for each house in the population was found to be 2.17. The lowest number of bathrooms found any of the houses was 1, and the highest 3.5. Age of each house was taken into...
Words: 2335 - Pages: 10
...HOUSE PRICES II CASE: 28 Olusegun Abebayo TAKSAMAI TANAPAISANKIT STACEYANN BARTON GM533 Applied Managerial Statistics Abstract Pricing your home competitively is an important factor in determining your selling price. As a seller, the aim is to get the best asking price. To prevent losing money, one has to be careful not to underprice their home. As mentioned in the article Selling Your Home – The Importance of Pricing Correctly, the most important factor when selling your home is not what your home is listed for, but rather what similar homes have recently sold for. This is the statistic that will properly tell you what buyers are willing to pay for a similar home, in a comparable neighborhood. In the article entitled Pricing Houses-Pricing Houses to Sell, Elizabeth Weintraub provided a few guidelines that can be effective in pricing one’s home. She suggested that a seller looks at every similar home that was or is listed in the same neighborhood over the past six months. Compare similar square footage, within 10% up or down from the subject property, if possible. Compare apples to apples. The objective of this study is to use the data given in Case 28 – Housing Prices 11 to determine the selling price for a house in Eastville, Oregon and prepare and establish the description of how the findings might be used as a general method for estimating the selling price of any house in my neighborhood. In doing so, we had to figure out what factors determine the selling...
Words: 6813 - Pages: 28
...RESEARCH ARTICLE Huayi Yu China’s House Price: Affected by Economic Fundamentals or Real Estate Policy? © Higher Education Press and Springer-Verlag 2010 Abstract Many theory and empirical literature conclude that house price can reflect economic fundamentals in the long-term. However, by using China’s panel data of 35 main cities stretching from 1998 to 2007, we find that there is no stable relationship between house price and economic fundamentals. House price has deviated upward from the economic fundamentals since government started macro-control of the real estate market. We consider that the mechanism between the house price and economic fundamentals is distorted by China’s real estate policy, especially its land policy. Meanwhile the policy itself is an important factor in explaining the changes of China’s house price. Then we estimate the dynamic panel data model on house price and the variables which are controlled by real estate policy. The result shows: land supply has negative effects on house price; financial mortgages for real estate have positive effects on house price; and the area of housing sold and the area of vacant housing, which reflects the supply and demand of the housing market, has negative effects on house price. We also find some differences in house price influence factor between eastern and mid-western cities. Finally, we propose policy suggestions according to the empirical results. Keywords house price, economic fundamental, real estate policy...
Words: 11366 - Pages: 46
...The Research Proposal For The Relationship among Shanghai Commercial Housing Price and Four Variables, Disposable Income, Completed Housing Area, Interest Rate, and Inflation Rate (2007-2010) BY FIN (2) Yang Bohan 0730200084 Tel: 13750016724 Guo Bingyu Liu Yuanjia Xia Jinjing Teng Linyan Li Hui 0730100034 0730100086 0730200079 0730200063 0730200148 Beijing Normal University – Hong Kong Baptist University United International College May 14, 2010 0 Table of Content 1. 2. 3. 4. 6. 7. 8. Title ........................................................................................................................................... 4 Introduction ..............................................................................
Words: 7950 - Pages: 32
...Should Chinese Men Buy a House? Mr. Zou, who did not get married until he bought a house when he was 35 years old, sold his house last year and earned an extra profit about 200,000 RMB. In China, much more men did not get married because they did not own a house; however, housing price keeps soaring. According to National Bureau of Statistics of China, from 2001 to 2011, during the 10 years, the average selling price of commercialized buildings in China has increased 147% from 2170RMB to 5357RMB per square meter, especially in metropolitan cities like Beijing, Shanghai which increased 243% and 310% respectively. Nowadays, most youth and their parents in China are concerned about the price of apartment and hesitate to buy it; however, during the hesitation, the price has reached a high record. In my opinion, Chinese men should not only buy a house but purchase it as soon as possible because of the impact of traditional custom, fierce marriage market and the best investment strategy. To begin with, if people especially men want to get married; they would better possess a house at first because of the traditional customs. Confucius, who was born in 2500 years ago, was a philosopher of the Spring and Autumn period of Chinese history. The philosophy of Confucius has a great, deep and persistent impact on each generation in China until now. One of his most famous proverbs is “to rightly govern the state, it is necessary first to regulate one's own family” (Dawson, 1915) which...
Words: 1647 - Pages: 7
...Business Statistics Received 96% Question 1 (20 Marks) A computer used by a 24-hour banking service is supposed to randomly assign each transaction to one of 5 memory locations. A check at the end of a day's transactions gave the counts shown in the table to each of the 5 memory locations, along with the number of reported errors. Memory location | 1 | 2 | 3 | 4 | 5 | Number of transactions | 82 | 100 | 74 | 92 | 102 | Number of reported errors | 11 | 12 | 6 | 9 | 10 | At 5% level of significance, is there sufficient evidence to conclude that the proportions of errors in transactions assigned to each of the 5 memory locations are all different? To use Excel Click Add-ins| PHStat | Multiple sample tests | Chi-square test | Question 2 (20 Marks) One criterion used to evaluate employees in the assembly section of a large factory is the number of defective pieces per 1,000 parts produced. The quality control department wants to find out whether there is a relationship between years of experience of employees and defect rate. Since the job is repetitious, after the initial training period any improvement due to a learning effect might be offset by a loss of motivation. A defect rate is calculated for each worker in a yearly evaluation. The results for 100 workers are given in the table below. Defect rate | Years since Training Period | | < 1 Year | 1-4 Years | 5-9 Years | High | 6 | 9 | 9 | Average | 9 | 19 | 23 | Low | 7 | 8 | 10 | At the 0...
Words: 1918 - Pages: 8
...value for each question is given in brackets. Remember that I will not regrade exams written in pencil, and that all problems with the grade must be brought to my attention before a week after I return the exams. Assumptions and critical values are stated in the appendix. No other formulas will be provided. 1 ˆ I. (18 pts) Consider y = β0 + β1 x1 + . . . + βk xk + u, and let {βj } be the corresponding OLS estimator. Choose between ’True(T)’, or ’False(F)’. (2 pts each) ˆ ˆ 1. If βj is consistent, E[βj ] = βj . 2. Under MLR.1 through MLR.5, the OLS estimator √ ˆ N (βj − βj ) is asymptotically normal. ˆ 3. If M LR.4 is replaced by M LR.4′ , βj is not consistent. 4. Even though MLR.6 is not true, we can use t−statistic when the sample size is large. 5. Changing the scale of the y variable will lead to a corresponding change in R2 . 6. If y is in the logarithmic form, changing the scale of the y variable will lead to a corresponding change in R2 . 7. If y is the family income measured in dollars, then it is mostly used in logarithmic form. 8. Standardized coefficient or equivalently beta coefficient measures the change in y when x is changed by one-standard deviation. 9. In the following model: log(y) = β0 + β1 log(x) + u, β1 is the elasticity of y with respect to x. 2 2 II. (30 pts) Multiple Choice Questions (3 pts each) 1. Under MLR.1 through MLR.3 and MLR.4’ ˆ a. βj is unbiased. ˆ b. βj is consistent. √ ˆ c. n(βj − βj ) is...
Words: 1189 - Pages: 5
...Richmond Housing Market Case 2 Surveys and Sampling A. Identify the population and variables. Population is the whole group of Richmond buyers interested to buy a house. Variables are: location, price, bedrooms, bathrooms, sq.ft, and realtor B. Identify variables as categorical or quantitative. * Location is categorical variable * Price is quantitative variable * Bedrooms are quantitative variable * Baths are quantitative variable * Sq.Ft is quantitative variable * Realtor is categorical variable C. What are some possible population parameters of interest? The parameter in this case is the 182 listing in Richmond, some other options to be considered are: average house price ($310,381), house size, how many bedrooms and bathrooms, location of the house, who is it listed by and the city zone. D. What are some possible sample statistics that could be calculated from this data? It is not necessary to calculate the statistics, just identify them. Average price for houses based on the county location, average household income, preferable characteristic and taste of the house buyers E. What is the sampling frame for the sample? The sampling frame is randomly selected from the single-dwelling properties for sale in the Greater Richmond area shown on the website realestate.aol.com. F. What is the sampling design? Systematic Random sampling (SRS) G. Are there any sources of bias in the sample? Yes – based on the data we have the direction...
Words: 317 - Pages: 2
...individual reports about two dwellings in two different postcodes and then a third part which compares the two dwellings and concludes with deciding which would be the best investment property with the most rental income. The first part of this report is focused on the postcode of 4214 which consists of suburbs such as Arundel, Parkwood, Molendinar and Ashmore. The price range that has been researched is between $500,000 to $600,000.The dwelling types that was given for this assignment was a house with 3 to 4 bedrooms, 2 bathroom and 2 garage car spaces. Refer to Appendix Five where you can see a picture of the property that matched this profile which I would purchase is located at 26 Petworth Court in the suburb of Arundel. It is a 4 bedroom, 2 bathrooms, with 2 garage spaces and 2 separate living areas. It has a pool, an outdoor entertaining area, has ducted air conditioning and is built on an 805m2 block of land (REA Group 2013). It has an advertised price of $595,000 with easy access to local shops and transport. The house comes is zoned as a detached dwelling which is just an average block of land for houses and is within walking distance of the local golf course....
Words: 3354 - Pages: 14
...# 28 House Prices | | A group of statistic student’s objective is to provide a business solution using statistical calculations and tools on a sample data. | | Upaiwan Porndumrongkit Ana Sanchez George Satiah Kritchapon Sopawatjirarich | 10/16/2010 | Executive Summary Summary: Home owners want to determine a reasonable asking price of a house based on a collection of home descriptions and its characteristics. However, home owners can get confused very easily as they see close variation in price based on different descriptive characteristics. Home owners hesitate to get professional advice from real estate agents due to service price. On the other hand, our Multi Regression Model can help real estate agents provide fast predictions and advice to home owners at a minimum service price. Our solution would give real estate agents a competitive advantage in the real estate market. Problem statement: How to determine a reasonable asking price on n number of houses with its descriptive characteristics. Solution: Multi Regression Model is one way to assist real estate agents use data to provide an estimate. Introduction The objective of this project is to provide the detailed data analysis and resolution to the problem statement mentioned above. As a group of students of statistics in the...
Words: 3700 - Pages: 15
...28: Housing Prices GM533 Managerial Statistics April 11, 2012 I’m conducting an analysis between the price of a home in Eastville, Oregon and the factors which develop a home’s price. The data is analyzed using ANOVA (Analysis of Variance) and multiple regression hypothesis testing procedures. The regression analysis will help create a multiple regression fit which will incorporate the ten predictor variables of a home’s price. After the regression analysis is complete, global and local ANOVA tests will help eliminate the insignificant predictor variables and create the net significant regression equation. Even though the sample size is only representative of the houses in Oregon, the general trends that affect house prices are the same. Therefore, this multiple regression model will be useful in its predictions but limited in it’s use on more complex homes. In addition, there are more factors than the ten discussed that affect the price of a home (i.e. the economy, inflation, etc.); therefore the tool has limited power. As a homeowner, one should know his or her home’s accurate price in the market. This is an extremely important fact to know, so a person can decide when it’s appropriate to sell their home. There are several tools out there already such as Zillow.com but, these websites are giving prices that are estimated from the original price of a home. Using a regression equation will give the ability to have a second opinion on his or her home’s price. Even though...
Words: 2098 - Pages: 9
...first set believes that they were advised to post asking-prices for their houses that were too low compared to other brokers’ listings while the second set believes that they were advised to accept selling-prices for houses that they purchased that were too high relative to the selling-prices for houses brokered by other realtors. Based on the given data for both VALMAX and the other brokers (will henceforth be called “OTHERS”), it is decided that the clients’ complaints are justified. In order to properly come up with this conclusion, the correct data must first be chosen, the null and alternative hypotheses must be stated, the degrees of freedom will be calculated, the rejection region will be defined, and ultimately, the test statistic will be calculated and interpreted. After this, there are three methods for coming up with the end conclusion, the first being qualitative and the last two being quantitative. ANALYSIS Choosing the Data: Three sets of data are given for both VALMAX and OTHERS for which we assume to be normally distributed: asking prices, selling prices, and the differences between the two. Either the asking/selling prices can be used or the differences data can be used, but not both. The reason why we have chosen to not use the asking and selling price data is because we cannot distinguish between the buying and selling agents. Also, we would have to assume that the asking and selling prices of VALMAX and OTHERS are in similar neighborhoods. Because...
Words: 1072 - Pages: 5
