1. Canon City, CO Real Estate Sales Forecast Model Katelyn Allenbaugh

2. Market & Data Sources  http://www.zillow.com/ 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/ 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/

3. Sold Houses Real Estate Sample Data Set Variables:12345678 MLSAddressBedroomsBath Total 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 635831271334 Poplar Ave33177610.52197411$210,0007/21/2014 635820511529 Poplar Ave41202021.21194812$90,0003/5/2012 635827601345 Poplar Ave31131220.43195621$79,00010/14/2013 635839951832 Poplar Ave33269520.4200712$265,1007/2/2012 635823161612 Logan St32158410.41196912$160,0003/28/2012 635821111611 Logan St21.5120120.29196922$130,0008/25/2014 635829571518 Logan St21106420.53195822$114,4008/21/2013 635946781514 Logan St31104020.21196312$102,90012/1/2014 635828221520 Birch St21105021.33195422$153,8008/14/2012 635828831537 Birch St41.5158810.31195822$115,0006/11/2012 635906561614 Birch St32143920.5198412$99,00012/22/2011 635829641545 Birch St2272020.23197322$60,00011/15/2012 635819661538 Lombard St32156012194311$133,0003/19/2013 635819641616 Lombard St31.5216021.39194811$100,0005/23/2014 635831691435 Lombard St43221410.32196012$239,9004/23/2014 635831771437 Lombard St21161410.32196412$128,0007/1/2013 635830621451 Lombard St31137610.31196112$114,90010/23/2013 635910551472 Stone Pl2192420.56197322$76,5003/24/2014 635910561482 Stone Pl33164720.63198912$79,9008/26/2013 635819841707 Chestnut St31.5280020.28196311$100,1257/25/2013 635823331531 Chestnut St31134420.45190912$65,0007/21/2014 635819751530 Chestnut St43339420.32200011$192,9004/8/2013 635830661523 Rosedale Ln2172020.12196022$37,5001/5/2012 635831481505 Oasis Ct32159510.5197712$180,50010/30/2014 1110825201427 Cedar Ave42215621.73197912$193,0008/15/2014 635819981644 Cedar Ave4219202 1.5195622$124,20012/21/2011 635824201630 De Elen Ct32160020.62197922$184,8006/25/2014 635829871203 Elm Ave32282221190012$235,0004/26/2012 635828551305 Elm Ave32220410.52197811$184,0007/18/2012 635910131201 Elm Ave32164911.94198411$149,9004/22/2013

4. Regression Analysis Model Variables 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 Statistics Multiple R0.837447489 R Square0.701318296 Adjusted R Square0.58753479 Standard Error36987.22709 Observations30 ANOVA dfSSMSFSignificance F Regression86745736778284321709736.1636200090.000385498 Residual21287291543221368054968 Total2996186522104 CoefficientsStandard Errort StatP-value Intercept-41680.07312743256.9334-0.0560776110.955810108 X Variable 1-24421.8830714049.69496-1.7382500570.09680959 X Variable 230417.8635614252.476992.1342159390.044775741 X Variable 371.6598618318.637633993.8449012290.000940767 X Variable 4-48903.082816824.5443-2.9066512550.008436623 X Variable 519393.1964514004.401481.3847929510.18065323 X Variable 618.04532822378.74769580.0476447210.962449604 X Variable 721924.2078319026.383991.1523055480.262144898 X Variable 845033.4506218119.613372.4853427990.021446049 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.

5. Final Regression Analysis Model Variables 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 R0.726940044 R Square0.528441827 Adjusted R Square0.452992519 Standard Error42594.61967 Observations30 ANOVA dfSSMSFSignificance F Regression450828981487127072453727.0039320910.000631422 Residual25453575406171814301625 Total2996186522104 CoefficientsStandard Errort StatP-value Intercept30261.464356816.104530.5326212440.598998542 X Variable 1-9854.94519815188.68568-0.6488346260.522361507 X Variable 236326.7539413695.081942.6525400930.013676591 X Variable 340.8215813618.552528742.2003243830.037243547 X Variable 41316.95320719969.209060.0659491920.947942841 RESIDUAL OUTPUT ObservationPredicted YResidualsStandard Residuals 1183,49326,5070.670247624 2110,945-20,945-0.529607716 393,215-14,215-0.359440774 4221,00844,0921.114895069 5139,32820,6720.522693108 6116,70213,2980.336240317 792,94621,4540.542468461 880,79522,1050.558945009 992,37561,4251.553174171 10112,7902,2100.05587139 11133,409-34,409-0.870062919 12115,231-55,231-1.39654018 13138,349-5,349-0.13524683 14144,678-44,678-1.129720911 15191,51848,3821.223373721 16114,08113,9190.351943142 1794,51120,3890.515553522 1887,231-10,731-0.271349883 19178,227-98,327-2.486262541 20170,804-70,679-1.787168538 2193,205-28,205-0.713170367 22239,687-46,787-1.183048795 2378,904-41,404-1.046921616 24139,77840,7221.02969488 25152,82340,1771.015889873 26144,507-20,307-0.513464063 27141,29943,5011.099962177 28189,86645,1341.141253169 29164,63819,3620.489584563 30141,9827,9180.200214934

6. 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.

7. 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.