'Linear Regression: House Pricing\r'

'Housing Prices in Blowing Rock, NC: A Hedonic Analysis doubting Thomas Carter Economics 4000 1. Introduction A intemperate character to understand round the trapping market is how a legal injury is given over for a particular base. That m iodinetary value go let oning be de stigmaated to that particular stand alone. both told holds have various wrong, so I seat’t always gestate that one will appeal more than or less than any other. The pricing for houses vary based on their characteristics. apiece characteristic must be analyse to conciliate its contribution or petty criticism toward the expenditure.I have taken some of these characteristics and simulate the relationship between them and the expense of reliable estate for a specific atomic do 18a. How are these characteristics use in determining the determine? A model that is commonly used in real estate judgement is the hedonic statistical regression. This method is specific to rift down items that are not homogenized commodities, to estimate value of its characteristics and ultimately determine a worth based on the consumers’ willingness to pay. The approach in estimating the values is through by measuring the differences in the price of certain goods with regards to specific location.E. g. honest cost of real estate is much lower berth in Missouri than in California. spatial relation whitethorn be the biggest factor in real estate pricing. 2. Data and statistical regression Analysis My data is for Blowing Rock, NC. It’s a resort town in the sacrilegious ridgeline Mountains. The attractions here are mostly outdoor activities taking place in the secluded wilderness. The population is only about 1500 and the average cost of a house from my data is $485,839. 50. For my linear regression, I am modeling the relationship between the price of homes, cosmos my dependent variable, and some characteristics of the omes, being my explanatory variables. Origin ally my data consisted of the undermentioned for real estate in Blowing Rock, NC: price †merchandising price, miles from central business district, effect of sleeping rooms, issue of abounding bathrooms, enumerate of half bathrooms, the year the home was built, square footage, trope of service departments, whether or not the house was find in a subdivision, lot size, if the house had a good view, issuance of old age on the market, and difference between postulation price and selling price. First I modeled a linear regression between price and all of my characteristics ( take hold of parry 1).To interpret these variables I have regressed, I look at the Coefficient column of the output. The sign of the number tells whether the characteristic increases or decreases the price. For distributively special mile away from the central business district the price of a home decreases $25,002. 96. For each additive bedroom the price increases $20,832. 78. For each additiona l full bathroom the price increases $79,715. 21. For each additional half bathroom the price increases $123,988. 80. For both year that a house ages the price decreases $2,355. 05. For e really increase in one square-foot the price increases $93. 13.For each additional service department the price increases $26,249. 66. If the house is in a subdivision the price increases $25,999. 07. For each additional acre of land the price increases 56,480. 75. If the home has a nice view(most likely of the Blue Ridge Mountains) the price increases 127,900. 10. For each additional sidereal day the home is on the market the price decreases $181. 04. Based on the adjusted R-squared I have determined that about 53. 38% of the price of homes in this town comes from these characteristics. smell at the P values, not all are earthshaking, thus some of these characteristics whitethorn play teeny part in determining the price.The insignificant characteristics were number of bedrooms, number of gara ges, and whether or not the home was in a subdivision. Some other washy variables were the number of days the home has been on the market and the difference between postulation price and selling price. I scent that the number of days the house a house is on the market is a weak explanatory variable because a seller usually has an idea of what the house is worth, and even if it does not sell immediately, they may be willing to wait or only need to adjust the price a little in target for it to sell.The difference in request and selling price could be correlated with the number of days on the market and very similar reasoning as to wherefore it is a weak variable. The seller will most likely not provide 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 cor related 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 agreeability 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. I took out the highly insignificant variables (bedrooms, garages, and subdivision) and modeled another(prenominal) regression (see Table 2). My adjusted R-squared change to 54. 28%. Expand! 3. Summary and ConclusionsTable 1 reg price miles bedrooms fullbath halfbath yearbuilt sqft garage sub solid ground vie > ws days diff consultation | SS df MS Number of obs = 100 ————-+—————————— F( 12, 87) = 10. 45 influence | 6. 0522e +12 12 5. 0435e+11 Prob > F = 0. 0000 Residual | 4. 2002e+12 87 4. 8278e+10 R-squared = 0. 5903 ————-+—————————— Adj R-squared = 0. 5338 Total | 1. 0252e+13 99 1. 0356e+11 Root MSE = 2. 2e+05 —————————————————————————†price | Coef. Std. Err. t P>|t| [95% Conf. Interval] ————-+————————————————————— †miles | -25002. 96 9499. 989 -2. 63 0. 010 -43885. 22 -6120. 706 bedrooms | 20832. 78 44293. 87 0. 47 0. 639 -67206. 08 108871. 6 fullbath | 79715. 21 40491. 55 1. 97 0. 052 -766. 1288 160196. 5 halfbath | 123988. 8 45920. 12 2. 70 0. 008 32717. 59 215260 yearbuilt | -2355. 046 1202. 24 -1. 96 0. 053 -4744. 596 34. 50387 sqft | 93. 13114 50. 65843 1. 84 0. 069 -7. 557963 193. 8203 garage | 26249. 66 28224. 21 0. 93 0. 355 -29849. 02 82348. 34 sub | 25999. 07 56280. 61 0. 46 0. 645 -85864. 75 137862. 9 acres | 56480. 75 13324. 99 4. 24 0. 000 29995. 88 82965. 61 views | 127900. 1 48592. 63 2. 63 0. 010 31316. 96 224483. 2 days | -181. 0406 126. 8538 -1. 43 0. 157 -433. 1762 71. 09506 diff | . 5086182 . 3190536 1. 59 0. 15 -. 1255353 1. 142772 _cons | 4541470 2363007 1. 92 0. 058 -155261. 1 9238202 Table 2 reg price fullbath halfbath yearbuilt sqft acres views days diff miles Source | SS df MS Number of obs = 100 ————-+—————————— F( 9, 90) = 14. 06 vex | 5. 9915e+12 9 6. 6572e+11 Prob > F = 0. 0000 Residual | 4. 2609e+12 90 4. 7344e+10 R-squared = 0. 5844 ————-+—————————— Adj R-squared = 0. 5428 Total | 1. 252e+13 99 1. 0356e+11 Root MSE = 2. 2e+05 —————————————————————————— price | Coef. Std. Err. t P>|t| [95% Conf. Interval] ————-+—————————————————————- fullbath | 84256. 29 38750. 63 2. 17 0. 032 7271. 402 161241. 2 halfbath | 131657. 9 43504. 03 3. 03 0. 003 45229. 58 218086. 3 yearbuilt | -2286. 429 1165. 349 -1. 96 0. 053 -4601. 599 28. 74033 sqft | 112. 8896 40. 74526 2. 77 0. 007\r\n'