Please help with questions C. D. and E. thanks in advance (previous answers atta
ID: 3335913 • Letter: P
Question
Please help with questions C. D. and E.
thanks in advance
(previous answers attached)
Here is a closer look at C, D, and E in case you can't see it
Here is the Linear Regression Model (this is the solved answer from a previous question)
Here is (Prescott_Parcel.xlsx)
price land_fcv age time pop_sqml gr_fl_ar Patio_Floor sedi_per_lake imp_fcv 245373.9 49096 5 10.66 4.17669 1710 0.106583 270.852 106186 305659.7 46497 36 10.73 4.17669 1652 0.098253 270.852 100628 260180.9 70629 8 11.42 4.17669 1967 0.098946 270.852 121079 243258.6 51534 27 11.44 4.17669 2352 0.089078 270.852 134085 269699.7 63992 17 11.79 4.17669 2037 0.097475 270.852 162465 301429.1 52793 11 12.11 4.17669 2472 0.092511 270.852 161584 138551.6 29921 26 18.13 203.668 960 0.117647 270.852 56544 185088 32404 40 17.36 203.668 1080 0.108911 270.852 80407 134321 50000 35 16.45 203.668 989 0.133216 270.852 77686 180328.6 29050 20 16.04 203.668 1320 0.10143 270.852 85571 169223.4 14800 29 20.05 203.668 1179 0.116854 270.852 74387 278160.9 46000 1 27.07 4.17669 2224 0.095935 40.83888 96717 185088 40500 17 11.4 203.668 1208 0.11437 270.852 91304 186674.5 40500 17 11.82 203.668 1208 0.11437 270.852 87569 181915.1 40500 17 11.71 203.668 1208 0.11437 270.852 81670 182972.8 62500 17 11.38 203.668 1208 0.11437 270.852 79853 343734.9 62500 10 11.87 203.668 3017 0.08907 270.852 223109 391752.1 27500 2 11.79 203.668 2555 0.084558 270.852 211511 332100.8 71000 5 29.22 4.17669 2137 0.102478 40.83888 205954 460869.3 5472 8 27.31 4.17669 2840 0.094099 40.83888 189398 222105.7 22553 16 14.96 4.17669 1478 0.10152 340.4167 139692 154310.5 20690 29 14.86 4.17669 1232 0.104651 340.4167 83611 132205.8 21261 31 14.52 4.17669 1232 0.104651 340.4167 66594 354311.4 54969 9 14.9 4.17669 1726 0.105236 340.4167 175794 135590.2 19000 31 6.53 222.533 1389 0.11246 340.4167 62691 134849.9 19000 31 6.5 222.533 1283 0.115782 340.4167 68674 145955.1 50000 39 6.98 222.533 1158 0.110599 340.4167 65503 195664.5 50000 13 7.09 222.533 1632 0.099338 340.4167 88900 261238.6 59375 10 18.73 59.1291 1914 0.099718 340.4167 156766 273930.3 59375 10 18.82 59.1291 1747 0.101337 340.4167 109027 344792.6 59375 9 17.75 4.17669 2063 0.100697 340.4167 160713 315707.3 66250 8 18.69 59.1291 2156 0.097152 340.4167 133659 333052.7 59375 9 19.04 59.1291 2016 0.099598 340.4167 168272 284506.8 59375 9 19.36 59.1291 2548 0.087393 340.4167 138439 327870.3 59375 9 19.49 59.1291 1876 0.10024 340.4167 133970 280276.2 69044 16 14.91 4.17669 1778 0.099747 340.4167 103570 409837.8 60000 8 6.53 222.533 2701 0.090266 19.51661 190565 242200.9 60000 5 5.74 222.533 2101 0.109746 19.51661 136186 169223.4 42000 1 5.7 222.533 1465 0.102878 19.51661 87630 216817.4 40000 8 5.56 222.533 2016 0.097179 19.51661 120646 449499.6 74888 9 19.29 59.1291 2007 0.097978 340.4167 198247 406664.9 69190 12 21.02 59.1291 2243 0.102082 340.4167 205632 391329 88872 9 21.11 59.1291 1870 0.131444 340.4167 170003 395559.6 88872 12 22.19 59.1291 2095 0.103168 340.4167 246569 433634.9 80178 7 20.37 59.1291 2258 0.099322 340.4167 168868 362772.6 80178 4 20.83 59.1291 2196 0.094059 340.4167 181785 317293.8 80178 10 20.47 59.1291 1786 0.125795 340.4167 126688 525650.1 104880 12 21.85 59.1291 3032 0.088942 340.4167 328893 793234.5 71225 10 19.88 59.1291 5167 0.070349 340.4167 441161 Yoo et al. (2014) recently studied the impacts of a variety of factors influencing house prices in Prescott, Arizona. Prescott metropolitan area in Central Arizona includes five recreational lakes Granite Basin Lake, Watson Lake, Willow Creek Reservoir, Lynx Lake and Upper Goldwater Lake. The five lakes in the study area provide a range of benefits recreational opportunity. scenic beauty, clear water, etc to the residents of Prescott, and hence casier access to those lakes is expected to increase ncarby residential property prices. Higher level of sedimentation in the lakc or rivers lcads to physical disruption of water quality and crcates high levels of turbidity by limiting penctration of sunlight into the water column. Thcrefore, highcr level of scdimentation is cxpccted to decrcasc ncarby residential propcrty valucs. Using a regrcssion model(s). you will investigate a varicty of cnvironmental, structural, and socio-cconomic factors that affect residential propcrty values using this dataset (Prescott Parcel.xlsx). Description of variables included in the dataset is shown below Description Variable Price Residential property sales in dollar (Year: 2003) Land fcv Land full cash value in dollar (reflection of the market value of your property and consists of land and improvements) The age of property Traveling time to the nearest lake in minute Time Pop sqml Number of population/mile in 2000 (mcasured on a Census tract level) Gr_11 ar Patio Floor The ratio of patio area to total arca (Total arca=ground floor arca+patio area) sedi per lake Tons of sediment loads/lake acre in the nearest lake from each residential property The area of ground floor (sq) Improvced full cash valuc Please address the following: Explain your aiswers. C. Are structural variables statistically significant and their sigs come out as expected? D. How would you expla the mpact of population density on housing price? E. Based (n he resul swhat would vou suggest property develorels, house buyers, or local goverimeni decision makers should do to improve the environmental and economic quality (welfare) of local community?Explanation / Answer
In this problem, Price is the dependent variable and the remaining variables in the dataset are the independent variables, using which this linear regression model has been fit.
Among the independent variables, the environmental variable (or factor) is sedi_per_lake. The socio-economic variables (or factors) are Land_fcv, Age, Time, Pop_sqml and Imp_fcv. The structural variables (or factors) are Gr_fl_ar and Patio_Floor.
(C) After the linear regression model has been fitted, we observe that among the two structural variables (present in the model) listed above, Gr_fl_ar has come out to be statistically significant, where Patio_Floor has not. This is because the variable Gr_fl_ar has a p-value less than 0.05, which can be observed from the regression output. The sign of Gr_fl_ar has also come out as expected, because, in real life as well, as the area of ground floor increases, the price of a residential property increases.
(D) From the regression output, we observe that population density, represented by Pop_sqml variable, negatively impacts the housing price. This means that if the population density decreases, the housing price increases and vice-versa. Further, we observe that the Pop_sqml variable has a regression coefficient of -56.1, which means that if the population per sq. mile increases by 1 person, then the housing price reduces by 56.1 dollars, assuming that the effect of other environmental, structural and socio-economic factors are constant.
(E) In order to improve the environmental quality of the local community, the local decision makers should try to reduce the amount of sedimentation in the lakes, since the sedimentation levels positively impacts the housing prices. If the sedimentation levels of the lakes go higher, then people will be unwilling to reside in the community. If people move out of the community, then the population density will decrease, which will further increase the housing price.
In case of property developers, they should try to keep in mind the following factors - the ground floor area and the ratio of the patio area to the total area, since these two factors positively impact the housing prices. They can increase the sizes of the premium properties (in terms of ground floor area and patio area) in order to earn more profits, which, in turn, will improve the economic quality of the local community.
In case of house buyers, they should observe the following factors - land full cash value, improved full cash value, age of the property and travelling time to the nearest lake. Among these, only the age of the property negatively impacts the housing price, whereas the remaining three factors positively impact the housing price. If the buyer buys a property which is old and is situated far away from the lake, then the full cash value of the property will be lower and, hence, the buyer can purchase that property at a considerably lower price.