Canon City, CO Real Estate Sales Forecast

ModelKatelyn Allenbaugh

Market & Data Sources

http://www.zillow.com/

http://www.zillow.com/homes/recently_sold/Canon-City-CO/house_type/3958_rid/pricea_sort/38.428395,-105.199349,38.410474,-105.231364_rect/14_zm/

Sold Houses Real Estate Sample Data SetVariables: 1 2 3 4 5 6 7 8

MLS Address Bedrooms BathTotal SQ Ft

Water Shares Total: Yes = 1, No

= 2

Lot (Acres)

Built Year

Garage: Yes = 1, No = 2

Shed: Yes = 1, No = 2

Selling Price

Date Sold

63583127 1334 Poplar Ave 3 3 1776 1 0.52 1974 1 1 $210,000 7/21/201463582051 1529 Poplar Ave 4 1 2020 2 1.21 1948 1 2 $90,000 3/5/201263582760 1345 Poplar Ave 3 1 1312 2 0.43 1956 2 1 $79,000 10/14/201363583995 1832 Poplar Ave 3 3 2695 2 0.4 2007 1 2 $265,100 7/2/201263582316 1612 Logan St 3 2 1584 1 0.41 1969 1 2 $160,000 3/28/201263582111 1611 Logan St 2 1.5 1201 2 0.29 1969 2 2 $130,000 8/25/201463582957 1518 Logan St 2 1 1064 2 0.53 1958 2 2 $114,400 8/21/201363594678 1514 Logan St 3 1 1040 2 0.21 1963 1 2 $102,900 12/1/201463582822 1520 Birch St 2 1 1050 2 1.33 1954 2 2 $153,800 8/14/201263582883 1537 Birch St 4 1.5 1588 1 0.31 1958 2 2 $115,000 6/11/201263590656 1614 Birch St 3 2 1439 2 0.5 1984 1 2 $99,000 12/22/201163582964 1545 Birch St 2 2 720 2 0.23 1973 2 2 $60,000 11/15/201263581966 1538 Lombard St 3 2 1560 1 2 1943 1 1 $133,000 3/19/201363581964 1616 Lombard St 3 1.5 2160 2 1.39 1948 1 1 $100,000 5/23/201463583169 1435 Lombard St 4 3 2214 1 0.32 1960 1 2 $239,900 4/23/201463583177 1437 Lombard St 2 1 1614 1 0.32 1964 1 2 $128,000 7/1/201363583062 1451 Lombard St 3 1 1376 1 0.31 1961 1 2 $114,900 10/23/201363591055 1472 Stone Pl 2 1 924 2 0.56 1973 2 2 $76,500 3/24/201463591056 1482 Stone Pl 3 3 1647 2 0.63 1989 1 2 $79,900 8/26/201363581984 1707 Chestnut St 3 1.5 2800 2 0.28 1963 1 1 $100,125 7/25/201363582333 1531 Chestnut St 3 1 1344 2 0.45 1909 1 2 $65,000 7/21/201463581975 1530 Chestnut St 4 3 3394 2 0.32 2000 1 1 $192,900 4/8/201363583066 1523 Rosedale Ln 2 1 720 2 0.12 1960 2 2 $37,500 1/5/201263583148 1505 Oasis Ct 3 2 1595 1 0.5 1977 1 2 $180,500 10/30/2014111082520 1427 Cedar Ave 4 2 2156 2 1.73 1979 1 2 $193,000 8/15/201463581998 1644 Cedar Ave 4 2 1920 2  1.5 1956 2 2 $124,200 12/21/201163582420 1630 De Elen Ct 3 2 1600 2 0.62 1979 2 2 $184,800 6/25/201463582987 1203 Elm Ave 3 2 2822 2 1 1900 1 2 $235,000 4/26/201263582855 1305 Elm Ave 3 2 2204 1 0.52 1978 1 1 $184,000 7/18/201263591013 1201 Elm Ave 3 2 1649 1 1.94 1984 1 1 $149,900 4/22/2013

Regression Analysis ModelVariables used in this forecasting model are: Number of Bedrooms & Bathrooms, the Total Square Footage of the house, the total number of water shares the house comes with, the size of the lot (acreage), the year the house was built, if a garage is present, and if the house has a shed or some type of separate storage building.

Regression StatisticsMultiple R 0.837447489R Square 0.701318296Adjusted R Square 0.58753479Standard Error 36987.22709

Observations 30

ANOVA  df SS MS F Significance F

Regression 8 67457367782 8432170973 6.163620009 0.000385498Residual 21 28729154322 1368054968

Total 29 96186522104      

  Coefficients Standard Error t Stat P-valueIntercept -41680.07312 743256.9334 -0.056077611 0.955810108X Variable 1 -24421.88307 14049.69496 -1.738250057 0.09680959X Variable 2 30417.86356 14252.47699 2.134215939 0.044775741X Variable 3 71.65986183 18.63763399 3.844901229 0.000940767X Variable 4 -48903.0828 16824.5443 -2.906651255 0.008436623X Variable 5 19393.19645 14004.40148 1.384792951 0.18065323X Variable 6 18.04532822 378.7476958 0.047644721 0.962449604X Variable 7 21924.20783 19026.38399 1.152305548 0.262144898

X Variable 8 45033.45062 18119.61337 2.485342799 0.021446049

The first 3 variables (bedrooms, baths, and total square footage) are the basic essentials potential house buyers think to look for first when narrowing down the data field. Since water is scarce and expensive, some potential buyers may wish to buy a house that has water right shares and the seniority level of those water rights. Another essential to house hunters may be the acreage size of the lot. Home-buyers may need a house that has a certain amount of land accompanying it. Some potential buyers may also be concerned when the house was built; buyers may not want to remodel the house or upgrade it to code standards. Furthermore, potential home buyers may be concerned whether or not a garage is present on the property; and depending on those buyers’ needs, they may make additional storage options a fulfilled requirement on their home-buyers’ requirements list.

Any other features included in listed properties only convince potential home-buyers to buy that particular property, after the main requirements are completed; or other feature benefits cause those buyers to negotiate their requirements.

Final Regression Analysis ModelVariables used in this forecasting model are: Number of Bedrooms & Bathrooms, the Total Square Footage of the house, and if a garage is present.

Regression Statistics

Multiple R 0.726940044

R Square 0.528441827

Adjusted R Square 0.452992519

Standard Error 42594.61967

Observations 30

ANOVA

  df SS MS F Significance F

Regression 4 50828981487 12707245372 7.003932091 0.000631422

Residual 25 45357540617 1814301625

Total 29 96186522104      

  Coefficients Standard Error t Stat P-value

Intercept 30261.4643 56816.10453 0.532621244 0.598998542

X Variable 1 -9854.945198 15188.68568 -0.648834626 0.522361507

X Variable 2 36326.75394 13695.08194 2.652540093 0.013676591

X Variable 3 40.82158136 18.55252874 2.200324383 0.037243547

X Variable 4 1316.953207 19969.20906 0.065949192 0.947942841

RESIDUAL OUTPUTObservation Predicted Y Residuals Standard Residuals

1 183,493 26,507 0.6702476242 110,945 -20,945 -0.5296077163 93,215 -14,215 -0.3594407744 221,008 44,092 1.1148950695 139,328 20,672 0.5226931086 116,702 13,298 0.3362403177 92,946 21,454 0.5424684618 80,795 22,105 0.5589450099 92,375 61,425 1.553174171

10 112,790 2,210 0.0558713911 133,409 -34,409 -0.87006291912 115,231 -55,231 -1.3965401813 138,349 -5,349 -0.1352468314 144,678 -44,678 -1.12972091115 191,518 48,382 1.22337372116 114,081 13,919 0.35194314217 94,511 20,389 0.51555352218 87,231 -10,731 -0.27134988319 178,227 -98,327 -2.48626254120 170,804 -70,679 -1.78716853821 93,205 -28,205 -0.71317036722 239,687 -46,787 -1.18304879523 78,904 -41,404 -1.04692161624 139,778 40,722 1.0296948825 152,823 40,177 1.01588987326 144,507 -20,307 -0.51346406327 141,299 43,501 1.09996217728 189,866 45,134 1.14125316929 164,638 19,362 0.48958456330 141,982 7,918 0.200214934

Final Regression Analysis Model This final model represents 4 variables that I felt were the most important assets that potential home-

buyers require when house hunting. The four variables I chose were:

Total Number of Bedrooms

Total Number of Bathrooms

Total Square Footage of the House

Whether the house has a garage on the property

In this final model, the R-Squared value decreased from the original regression model using all 8 variables.

Although the F value is not highly significant, it did increase from the original model; causing the Significant F to decrease. Normally, the higher the F value and lower the Significant F, the better the model; however, I believe that the variables used in this final model have a more realistic, significant view of potential house buyers, thus I ignored the other models that showed greater F-values.

The P-value in two of the variables (Number of Baths & Total Square Feet) are not as low as the original model’s low values, however, these figures are still low enough to affect the final model. The other two variables (Number of Bedrooms & Garage Present) are not significantly low values and normally indicate that they will not greatly affect the model if removed. Nevertheless, I chose to leave the to variables in since I believe they are key factors to house hunters.

As shown in the given data table, 27 out of 30 of the sample data houses will lose their selling price value while 13 out of 30 will increase in selling price value. This is indicated through the predicted y column by using the residual analysis column’s values. Subtracting the residual’s column value from the main selling price of the house, the predicted values are determined and the predicted outcome of the selling price is indicated. Thus, this final model is predicting which individual properties will lose their price value and which ones will increase in their selling price value.

Final Model Application

Since this model’s elements indicate that this is not a very reliable model, I am somewhat skeptical at relying on this particular regression model for predicting the future selling prices for these data sets in Canon City, Colorado.

Nevertheless, the process for predicting the future selling prices of these houses remains the same. Thus applying a reliable regression model to a data set will be useful in predicting the future selling prices of houses in this location. Though the data sample may include different city locations or a larger number, the variables used in the final model would be the most significant key factors in helping create a regression model for future house sale prices.