1. University Park Real Estate Analysis Real Estate & Homes For Sale - Zillow. (n.d.). Retrieved December 2, 2014, from http://www.zillow.com/homes/for_sale/38.322678,- 104.583632,38.31027,-104.60466_rect/15_zhttp://www.zillow.com/homes/for_sale/38.322678,- 104.583632,38.31027,-104.60466_rect/15_z Cooper Hoag BUSAD 360

2. Sample Data Set AddressBedroomsTotal SqFtSelling PriceGarageGarage SqFtDetatchedAttachedNoneBathLot SqFt 4331 Fireweed Dr. 81001 42496$198,000Attatched48001046868 4710 Scarlet Sage Dr. 8100142685199900Attached47501047996 27 Terrace Dr. 8100141588143900attatched44001027075 6 Erica Ct. 8100154511525000Attached782010313100 10 Woodsorrel Ct. 8100142463204900Attached448010310018 8 Gleneagle Ct. 8100153749338000Attached871010410900 3 Ridgewood Ct.54202329999Attached7500103.511000 4 Mayweed Ct. 8100162200209000Attached54001039583 4604 Scarlet Sage Dr. 8100141600179000Attached4900102.59854 3 Cornflower Ct. 8100141700184950Attached5760102.58626 7 Cornflower Ct. 8100131656167000Attached52801038308 4711 Desert Candle Dr. 8100154322439900Attached77201048668 98 Ironweed Dr. 8100143485269900Attached52901049278 4709 Bayweed Ct. 8100134108399000Attached704010410684 6 Cornflower Ct. 8100162348198000Attched480010313900

3. Regression Analysis (Model Runs, Variable Selection) SUMMARY OUTPUT Regression Statistics Multiple R0.970039 R Square0.940975 Adjusted R Square0.908183 Standard Error328.1475 Observations15 ANOVA dfSSMSFSignificance F Regression515449781308995628.69552042.83317E-05 Residual9969127.1107680.8 Total1416418908 Coefficien ts Standard Errort StatP-valueLower 95% Intercept-1299.66761.1465-1.70750.121908712-3021.492026 Bedrooms50.54139114.94760.4396910.670529856-209.4881211 Selling Price0.0068210.0015864.299810.0019912080.003232606 Garage SqFt0.2255951.1760060.1918320.852132392-2.43471458 Bath521.4104152.33463.4227960.007594264176.8055485 Lot SqFt0.0292710.0600550.48740.637626545-0.106582871 SUMMARY OUTPUT Regression Statistics Multiple R0.819400085 R Square0.6714165 Adjusted R Square0.616652583 Standard Error670.5086622 Observations15 ANOVA dfSSMSFSignificance F Regression2110239265511963.00312.260198680.001258562 Residual125394982449581.8661 Total1416418908 Coefficients Standard Errort StatP-valueLower 95% Intercept-1169.737322984.5899-1.1880452570.257798068-3314.974357 Garage SqFt5.7568993561.3690614.2049971510.0012210082.773970777 Lot SqFt0.0659829910.0985330.6696543370.515756917-0.148701778

4. Description of Final Model In the original model, the f-stat being at 28.69 is very average, r- squared is related to the model, p-value shows that the different variables matter, t-stat signal that beds, lot sqft, and garage sqft are the most related and most important. The second model for r-squared at.67 is average and the low f probably means that it was luck or chance that it was this low. T-stat for lot sqft is more reliable. The P-stat doesn’t say anything significant but says that the variables are relivent.

5. Residual Analysis I do believe I messed up with creating my equation or at least using the wrong kind of variables but this graph is comparison of Selling Price (orange) and Predicted Selling Price (blue). The formula I used for predicting selling price is under the chart. Y^=38110.05+50.4*total sqft-27994.5*number of bedrooms+3.7*lot sqft

6. Model Application AddressTotal SqFtBedroomsSelling PriceGarage SqFtLot SqFtBathGarageDetatchedNoneAttached 4331 Fireweed Dr. 81001 24964$198,00048068684Attatched001 4710 Scarlet Sage Dr. 810012685419990047579964Attached001 With the first house the predicted sales come out to be $77342.05 and the second comes out to be $91041.25. These prices are far from a realistic prediction. I became lost on which variables to use so I just used ones that I thought were the most relevant to what consumers are looking for. Obviously that wasn’t the correct way to go about it but other variables had even more drastic outcomes so I stuck to this way.