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_zCooper Hoag BUSAD 360

Sample Data Set

Address Bedrooms Total SqFt Selling Price Garage Garage SqFt Detatched Attached None Bath Lot SqFt4331 Fireweed Dr. 81001 4 2496 $198,000 Attatched 480 0 1 0 4 68684710 Scarlet Sage Dr. 81001 4 2685 199900 Attached 475 0 1 0 4 799627 Terrace Dr. 81001 4 1588 143900 attatched 440 0 1 0 2 70756 Erica Ct. 81001 5 4511 525000 Attached 782 0 1 0 3 1310010 Woodsorrel Ct. 81001 4 2463 204900 Attached 448 0 1 0 3 100188 Gleneagle Ct. 81001 5 3749 338000 Attached 871 0 1 0 4 109003 Ridgewood Ct. 5 4202 329999 Attached 750 0 1 0 3.5 110004 Mayweed Ct. 81001 6 2200 209000 Attached 540 0 1 0 3 95834604 Scarlet Sage Dr. 81001 4 1600 179000 Attached 490 0 1 0 2.5 98543 Cornflower Ct. 81001 4 1700 184950 Attached 576 0 1 0 2.5 86267 Cornflower Ct. 81001 3 1656 167000 Attached 528 0 1 0 3 83084711 Desert Candle Dr. 81001 5 4322 439900 Attached 772 0 1 0 4 866898 Ironweed Dr. 81001 4 3485 269900 Attached 529 0 1 0 4 92784709 Bayweed Ct. 81001 3 4108 399000 Attached 704 0 1 0 4 106846 Cornflower Ct. 81001 6 2348 198000 Attched 480 0 1 0 3 13900

Regression Analysis (Model Runs, Variable Selection)

SUMMARY OUTPUT

Regression StatisticsMultiple R 0.970039R Square 0.940975Adjusted R Square 0.908183Standard Error 328.1475Observations 15

ANOVA df SS MS F Significance F

Regression 5 15449781 3089956 28.6955204 2.83317E-05Residual 9 969127.1 107680.8Total 14 16418908

Coefficien

tsStandard

Error t Stat P-value Lower 95%Intercept -1299.66 761.1465 -1.7075 0.121908712 -3021.492026Bedrooms 50.54139 114.9476 0.439691 0.670529856 -209.4881211Selling Price 0.006821 0.001586 4.29981 0.001991208 0.003232606Garage SqFt 0.225595 1.176006 0.191832 0.852132392 -2.43471458Bath 521.4104 152.3346 3.422796 0.007594264 176.8055485Lot SqFt 0.029271 0.060055 0.4874 0.637626545 -0.106582871

SUMMARY OUTPUT

Regression Statistics

Multiple R 0.819400085R Square 0.6714165

Adjusted R Square 0.616652583

Standard Error 670.5086622Observations 15

ANOVA

df SS MS F Significance F

Regression 2 11023926 5511963.003 12.26019868 0.001258562

Residual 12 5394982 449581.8661

Total 14 16418908

CoefficientsStandard

Error t Stat P-value Lower 95%

Intercept -1169.737322 984.5899 -1.188045257 0.257798068 -3314.974357

Garage SqFt 5.756899356 1.369061 4.204997151 0.001221008 2.773970777

Lot SqFt 0.065982991 0.098533 0.669654337 0.515756917 -0.148701778

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.

Residual Analysis

1 2 3 4 5 6 7 8 9 10 11 12 13 14 150

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Chart Title

Predicted Selling Price

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

Model Application

Address Total SqFt Bedrooms Selling Price Garage SqFt Lot SqFt Bath Garage Detatched None Attached4331 Fireweed Dr. 81001 2496 4 $198,000 480 6868 4 Attatched 0 0 14710 Scarlet Sage Dr. 81001 2685 4 199900 475 7996 4 Attached 0 0 1

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.