USER MANUAL August 2014
Contents
1. The data spanned six sixty day periods You will just have to select them when you are ready Regression for 13 13 ina Value 154 114 98 50 Observations R2 0 0 936 Adjusted R2 0 917 CoD Rating 5 4 Valuation Settings Selected Variable Intercept GLA BR Baths Age M DaysSinceSale MCA60_1 MCA60_2 MCA60_3 iv MCA60_4 MCA60_5 MCA60_6 Click on Valuation then Market Condition Analysis Value Weight 16 820 71 5 98 85 59 9 447 98 9 10 113 43 7 3 290 04 19 118 34 0 10 858 72 0 42 491 13 0 15 765 77 0 9 371 47 0 20 774 38 0 34 073 04 0 37 The following interface will appear Choose the Chart Choose the best fit but keep it as low as possible while still telling the story Print a report or copy directly into your appraisal report ZA Market Conditions Analysis Display Chart Value Trend O Market Activity Trend Best Fit Degree Date of property valuation Monday April 20 2009 300 000 00 188 400 00 160 500 00 132 600 00 104 700 00 76 800 00 48 900 00 21 000 00 Perform Market Adjustment Click here to adjust all of the sales used in the regression analysis for time and then re run the regression analysis based on the time adjusted sales price 38 Market Conditions Analysis Comments Put comments here that you want in the report Manu2. 2014 Automated Valuation Technologies Inc 439 Sun Valley Drive Maryville TN 37801 Send mail to P O Box 5839 Maryville TN 37802 TABLE OF CONTENTS Contents PURPOSE EE E E suf ation et acdaneduscuatentensdie suds alivectacvedasuduatectontcds uth slincehentedess dbbatectoavcds EEE et COMPONENTS iarere ine a EEE AANEEN EAE el adhd alae aed THRESHHOLDS aa ea na ea a E T E E Aa a AEE E EE s aN IMPORT FEATURE A TAE EE E E DATASCRUBBER niaan ea a a Ea aaa E a a E a E T DEFINE THE VARIABLES S cies cevneiedececouecs atinae aiaa ARRA ccveukecldbediiee cies uauentvedete AEEA ea REAA a ANa SAREA anA PROPERTY VALUATION PARAMETERS scceceessececeesaceeeeeaeeeceesaceesessaeeeseeaaeeeeesaaeeesesaaeeesesaeeeeesaa REGRESSION SCREEN eis csvc ccssctecivcccit canesvecndceeet nnssbesinsciet crnsssvecatsstet nisabesitseeit etnsesteandeeet EEEE ENN TREND ANALY SIS 2 253 aoe ace a he ee ae ek ae a ae en eee CONFIDENCE RATING CHECK LUS Tiatoa E aa ara ARNAR E EOE T cettevectestedeed PRINT Se SAVES setts ER RE A AAEE sha citennadedt suetvien cealsh eeivennte at ea etwtsn eeadlse STEPS TO PERFORM A REGRESSION ANALYSIS 00 eessceeeessececesseeeceeaeeecessaeeeceesaeeeeeesaceeeeesaeeeseenaeeeeesea MARKET CONDITIONS MODULE 0 eeeccceessececeeseececeeseneececeenaeeeeeeaaeeecseaaeeeeeeaaeeeeneaaeeeeseaaeeeseeaaeeeeeeaaes PURPOSE The purpose of the Regression application is to provide all of the power of a reg
3. Any removed record can easily be added back in by a simple click Numerous readings to help complete the analysis a The number of records considered in the sample Coefficient of Variance a measure of the Market Model s predictive power The R squared and the adjusted R squared The weight assigned to each property component by the model The P factor for each property characteristic The standard error of the regression and for each variable considered A measure of each of the subject property s components fit to the components in the data sample SubDev h Ameans of identifying sudden changes in the value relationship of each property component by Residual Trend Analysis Perform Step wise or reverse step wise methods of analysis by simple clicks of a button Select or deselect any record on the fly A comprehensive checklist to help the user assign an Over all confidence rating to the prediction A comment box for any narrative that the user wishes to include in the report The Valuation Settings a Where the subject property s characteristics independent variables are entered b Add or deduct for any property characteristics not considered in the regression analysis Set the rounding of the final value mhogan THRESHHOLDS The P Values and Subject Deviation values are coded as follows P Value Green lt 0 1 Red gt 0 3 Yellow otherwise between 0 1 and 0 3 Subject Deviation Green lt 1 0 Yellow g
4. Observations 6 Weights Applied by Model 8 P Values of the Coefficients 9 Curvilinear Test of the Variables v The Over all Confidence Rating Additional Comments Y 2 Quality amp Accuracy of Data Sample 3 Model s Prediction Accuracy CV amp Avg Residual v4 Subject Property s Fit to the Data Sample 7 R Adjusted R and Standard Error 5 Reasonability of Model s Outputs for Characteristics 10 Anderson Darling test for the normality of the residuals Okay X Cancel The purpose of the Check list Rating System is to organize information so that an opinion can be formed about the reliability of the final value estimate for the subject property The reliability of the value prediction for the subject property is based on 1 The quality robustness of the model and 2 how well the subject property fits the sales data used in the model This check list can shed some light on the reliability of the individual coefficients value assigned to each property characteristic but it is not a complete check list for that purpose Each item on the check list is addressed below when used to form an opinion as to the reliability of the predicted value for the subject property 27 Number of observations The number of sales needed depends on a lot of things like the amount of random variance in the market the quality of the data the number of variables which affect value the simi
5. the regression analysis The data table there is read wright The blank fields will be highlighted in a red color e You can omit the incomplete record row at several points during the scrubbing process Step 2 Scrub Data Scrubbing Feature 2 Calculate the Days Since Sale This feature is found on the Step 2 Scrub Data drop down A date field is not a number format so it must be converted to Days Since Sale V Calculate the number of days since the sale date Click here to open a calendar Column Indicating the Sale Date Date of Appraisal Wednesday April 11 2012 E Click on the dropdown arrow and select the name of the field that contains the dates Next identify the date of the appraisal opening the calendar and then selecting the appropriate day The Days Since Sale is calculated for each record sale by subtracting the date of sale from the appraisal date Scrubbing Feature 3 Divide Dates into Dummy Fields This feature is found on the Step 2 Scrub Data drop down It will automatically set up and populate dummy fields time spans based on the days since sale The goal is to be able to trend values over time when there is a curvilinear relationship between the dates of sale and value The algorithm considers the time span from the oldest sale date to the date of the appraisal This total interval is then divided into periods 60 90 120 or 180 day spans This allows for the adjusted sal
6. to gauge the robustness of the model You will get a feel for which items markets typically put the most weight on In most of my markets the GLA for a residential dwelling is given the most weight usually around 60 So know that if a model recognizes the GLA to have only 15 then know that the model is not very robust R2 Adjusted R2 and Standard Error The R and Adjusted R will be in the range of 60 69 for Acceptable 70 79 for Good and gt 79 for Excellent The standard error is a type of residual measurement The lower the better however do not try to minimize this when model building P Values Color codes of mostly yellow and no reds is Acceptable Mostly greens and no reds is Good and all greens is Excellent The MCA modules a series of dummy fields can still be meaningful even with moderately high P values 29 Trend Analysis of Individual Components This is the test for curvilinear relationships between a property characteristic and value Small amounts are acceptable In the test itself an amount of variance of up to 3 is inconclusive Variances exceeding the range of 5 8 indicate some curvilinear relationship The Anderson Darling test for the normality of the residuals When using less than 30 40 observations the normality of the residuals must be confirmed The A D test is a well known and accepted test A value of 0 5 or greater indicates that the residuals follow a no
7. 0000 ooo 0 0 oO 0 150000 160000 MCA120_2 MCA120_3 MCA120 4 MCA120 5 MCA120_6 ooo Oi Oi Oi O O O Click these buttons to 170000 Predicted Vz 106283 17 130086 34 107759 65 1 98181 49 87769 26 105651 56 123268 54 72871 35 835 47 change the order of the sales 24 This is the Use this button to remove value that outliers automatically starting the with the record that has the regression largest absolute residual analysis predicts Value 585 000 Observations 105 Standard Error 46 863 R squared is the amount of the market s behavior that is described by the regression model The Adjusted R squared is a more conservative approach to R squared Standard Error Is for the regression analysis Coef Of Variation is the coefficient of variation Avg Abs Residual is the Average R 0 857 Coef of Variation 9 2 Absolute Residual Adjusted R 0 841 Avg Abs Residual 7 0 A D Normality 0 559 A D Normality is the Anderson Valuation Settings Confidence Darling test for normality of the Remove Interval residuals Outlier z oy a Set Valuation Confidence Rating 90 Selected Variable Coefficient Weight SubDev P Std Err Conf Int 7 Intercept 105 243 15 0 001 30795 305 51 158 The Valuation Settings button allows you to enter the subject information The Set Valuation Confidence Rating button allows you to complete the confidence check li
8. REGRESSION ditions module with market CO z pata Soru ubbing TOO S USER MANUAL August 2014 Automated Valuation Technologies Inc Regression For Real Estate Professionals with Market Conditions Module amp Data Scrubbing Tools This Regression software program and this user s manual have been created by Automated Valuation Technologies Inc AVT The purpose of AVT is to fill the voids in appraisal practice that result from the rapidly changing appraisal environment Appraisers often find themselves engaged in new activities which require the use of technology that has not yet been created This is both unfortunate and unacceptable It is unfortunate because appraisers are not fully effective in carrying out their duties It is unacceptable because it compromises the vital role appraisers perform in the safekeeping of their country s greatest wealth real property It is AVT s mission to provide the technologies real estate appraisers require to fulfill their duties AVT operates under the belief that there is no substitute for the Neighborhood Appraiser Their knowledge of the local market is unique and cannot be duplicated by remote computer analysis These local appraisers are hardworking and dependable Without question these gritty individuals will carry out their duties as long as they have the knowledge and tools to do so This manual and the accompanying software program are copyrighted
9. ally Adjusted Values Sale Date Sale Price Diff DiS Adj Price 010909 234 500 00 43 1 101 108 133 392 00 Ba Copy Manual 3 Print Manual Report Report Enter comments for the report here Enter the date and sales price of the sales comparables here and the time adjustments will be returned based on the trend line for use in the direct sales comparison approach Note For illustrative purposes the example used here has an extreme fall in the value trend hopefully your real trend line will not fall so steeply 39
10. andled by solving equations therefore all pertinent data must be converted to a number format so the computer can perform the calculations This includes dates however Excel will automatically convert the dates to a number format in its built in regression module A blank or empty data field is not allowed in regression Typically any field should be omitted where all of the properties records observations have the same value in one field An example would be if all of the observations had three bedrooms Keep in mind that you can do all of your scrubbing in Excel and then copy and paste the scrubbed data directly into the Regression and skip the automatic scrubbing features DATA SCRUBBER Scrubbing Feature 1 Add Additional Records vv wii bLervewia LV maoy This is found on the Step 1 Load Data drop down 2351 Sir Edward Lane Mann 2414 Dublin Drive Mary oa said DUBLIN DRIVE con Once the data has been loaded you can add additional 3429 Dublin Dr Mann records by clicking the New Row button and then entering the data into each field You must use the r z Tab key to move to the right and the Shift Tab e to move to the left 7 Note The data table is read only so once you leave the new row you just entered you cannot go back and edit it If you leave a row and need to edit it you can e Close the window and simply reload the data e Leave as is and then complete the fields in
11. at one or more of the fields are blank in that column This has to be dealt with sooner or later as the regression formula cannot run if even one field is blank In cases where you have an abundance of data you could check this option and any records sales that have a blank in that column will be removed It is alright not to check this field and the Regression will load the record but automatically uncheck the record and highlight the field in Red so you can easily locate it and fill in the proper entry and select the record for inclusion in the analysis 21 DEFINE THE VARIABLES Identify and choose the data to be used J Create a Regression Document Je Step 4 Define Variables Select the fields columns from the list on the left and move them into the roles on the right that those fields will play Usually the property sale price Sales Price Independant Variables The fields on which to do the regressi Baths Descriptive Columns contain information like address or listing number This information is not used in the analysis The Dependent Variable is what you are trying to find such as sales price rent per square foot etc The Independent Variables are the things that are analyzed to predict the dependent variable 22 PROPERTY VALUATION PARAMETERS Set the Set the Rounding Decimals here here Property Vate Output Value Dis
12. bine Columns You may combine several columns into a single column either by concatenating them or by using a mathematical expression Double click a column name in the list of available columns in order to add itto the expression Combination Type Enanemaial New Column Name GLA Expression Main Upper pS Available Columns Baths Full Baths Half Garage Main Pool Year Built Closing Date Qala Drinn A In this example the new field will be named GLA It sums the square feet in the fields named Upper and Main The entry for the new field might be 2 345 square feet fields will not be removed The original 14 Scrubbing Feature 8 Data Transformation This feature is found on the Step 2 Scrub Data drop down To access this feature select the Add New Combination button On the drop down select the fx Mathematical Button and give a name for the new field Then select the field you wish to transform by double clicking on the appropriate field name Transformation methods are limited by the mathematical operators available to direct the calculation Valid numerical combination operators are Modulus and round parentheses Logarithmic transformations are not available Data transformation is primarily used to aid the regression formula Multiple Linear Regression deal with a curvilinear relationship Data transformation is an intermediate lev
13. ct property s residual to be The Regression indicates the subject s deviation in terms of standard deviations from the sales for each property characteristic by the SubDev rating In general the lower the SubDev rating for the subject the lower the subject property s residual would be expected to be This is especially true for the property characteristics which have the greatest weight in the model 28 Reasonableness of Model s Outputs for Characteristics This is the coefficients for each variable or property characteristic Appraisers that are familiar with a market have some idea of a reasonable value per unit of a characteristic For example if a model returns a coefficient of 43 000 for a double attached garage then the model is not very robust regardless of how good the residual analysis looks A value of 22 00 per square foot of GLA would not appear to be reasonable for most markets However be aware of oddities that may be legitimate For example once appraised a large 100 year old home In this market it was popular to purchase these older homes and re4store them to their earlier grandeur This process was very expensive based on the craftsmanship required found that in this market GLAs over 2 500 square foot had a negative value After careful consideration believe that buyers simply cannot afford to restore houses larger than 2 500 square feet Weights Applied by the Model Again here we are trying
14. d of the date of sale as a date is an illegal format b If your MLS or other data source has a field for full and half baths the scrubber can combine these fields for you c The year built is best converted to age as this will result in a smaller intercept d The scrubber will convert a column of text entries into numeric columns for each unique value e The scrubber will add two columns together and create a new column for the sum 4 Transfer the data to the Regression by either a Directly from the Excel workbook via a browser or b Copy the data to the MS Clipboard and paste into the Regression Set up the scrubber Identify the columns to be used by categories a Descriptive b Dependent Variable c Independent Variable 7 Load the data into the regression analysis and check the data for a Blanks b Text and c Outliers 8 Enter the subject information 9 Perform the regression analysis by adding or subtracting variables and checking the Trend Analysis for each individual variable used 10 Complete the confidence rating check list 11 Report a Print out the report b Copy the report into MS Word or your appraisal report c Convert to an Adobe pdf file 32 MARKET CONDITIONS MODULE The Regression program is a multi linear system This system is easy to understand and works reasonably well for most real estate applications However there are times when a property characteristic simply does not conform to a s
15. ed to 3 half baths It would be alright to not combine these two fields if the analyst is trying to find the coefficient for both categories Most markets have limited sales making it impossible to extract line item adjustments rate for baths This is probably because of the high degree of multicollinearity but also because buyers may rate baths as either acceptable or unacceptable rather than valuing them on a per unit basis 11 Scrubbing Feature 5 Calculate the Age of Property This feature is found on the Step 2 Scrub Data drop down Many databases store the age of the property by the Year Built Using the year built is mathematically sound but will result in a very large intercept value For this reason it is best to calculate the age of the property at the date it sold The calculation is based on the Sale Date less the year built In this example below the database uses Year Built and Closing Date to make this calculation V Calculate the age of the property at time of sale Column listing Year Built Year Built x Column Indicating the Sale Date Closing Date 7 12 Scrubbing Feature 6 Concatenate Multiple Fields This feature is found on the Step 2 Scrub Data drop down To access this feature select the Add New Combination button Closing Date Combine the following columns lt On the drop down select the A Textual Button and gi
16. eened B Yard Sunroom Porch Patio FA v v 7 7 a v Vv T E 7 7 v E v v E v v v E m E o D Porch Deck Enclosed v v W F A new column will be created automatically for the first 10 fields The user can add more fields by clicking the Add Fields button opening the following drop box Occasionally the scrubber will not be able to choose the correct delimiter and the fields that are built automatically will not be useful In this case you will have to delete the fields and use the Add Field button to type in the word or phrase you are looking for You can either type in a word or phrase you are looking for or select one of the fields in the drop list The scrubber will create the field and check the checkboxes if the record has the property characteristic Add New Field What would you like the new field to be called 2 Sunroom B Patio L C Deck D Porch Enclosed E Porch Covered F Gas Grill G Wood Fence H Chain Fence J Swimming Pool Above Ground K Storm Windows L Insulated Windows M Aluminum Windows O Bay Window P Storage Shed Q Professional Landscaped D TV Antanna 20 Notice that some fields have the option to Filter records in which this field is blank This means th
17. el activity and should not be attempted by novice analysts To edit any of the combined fields double click on it To omit the function simply uncheck it To delete a combination select it and press the Delete key 7 Combine the following columns gt Add New Combination V A Address Street Number Street Name Town V fe GLA Main Upper 15 Step 3 Scrub Data Some More Scrubbing Feature 9 No Change This feature is found on the Step 3 Scrub Data Some More drop down When the No Change is selected then that field is not modified and will be available to transfer into the Regression for analysis as is Scrubbing Feature 10 Exclude Field This feature is found on the Step 3 Scrub Data Some More drop down When this is selected the field will not be carried forward to the regression analysis 16 Scrubbing Feature 11 Vertical Rating This feature is found on the Step 3 Scrub Data Some More drop down It is used 1 To rate specific property characteristics up to 6 levels 0 5 2 To convert a Yes No to 1 0 As an example for rating the property characteristic consider automobile storage You might have the following types and ratings Rating Keep in mind that the values must be lowest for O and highest for 5 Also regression outputs will be based on there being an equal amount of value between each number rating If
18. es prices to be charted over time indicating the trend in values It is important that there are multiple sales in each time span If not the time interval must be increased The longer the time spans the more sales are likely to have occurred during that period V Divide sales date into periods for Market Conditions Analysis MCA Column Indicating the Sale Date Closing Date Select the number of days you wish each time interval to be Time interval span days When leaving this page the scrubber will alert you to the number of hits that are in the first MCA period and the lowest number in the other MCA periods 10 Scrubbing Feature 4 Combine Full amp Half Baths This feature is found on the Step 2 Scrub Data drop v Combine the total number of full and half bathrooms down Many multiple listing Column listing Full Bathrooms Baths Full 7 services store baths in two Column listing Half Bathrooms categories full baths and Street Street e half baths Once these pirmai ai Town contains the half bath fields are identified the count Regression has an Raulston View algorithm which treats blanks as a zero and totals the sum of the full baths with the sum of the half baths For example 2 Full baths and 3 half baths will total to 4 5 baths The scrubber will also convert the half bath field where they are entered in a decimal format For example a 3 would be convert
19. larity of the subject to the sales used etc However as a rule of thumb 25 35 might be acceptable 36 49 Good 50 and more Excellent Quality and Accuracy of Data Sample The appraiser should have a feel for the quality of the data source For example if the sales were all inspected and measured by the appraiser and the sales terms have been verified then the data would be of Excellent quality If the sales came from an MLS where the sales agents inconsistently handle the basement square footage and the subject and or some of the sales have basements then the data may not even be of Acceptable quality Model s Prediction Accuracy CV amp Avg Residual This is the model s prediction accuracy as measured by residual analysis The coefficient of variation and the average absolute residuals are measures of the disbursement of the residuals The range of random variance should also be considered this is taken from the Residual Scatter Plot In model building it is important not to strive to get the residuals as low as possible The average absolute residual coefficient of variation and the maximum random variance will not typically be below 5 7 and 12 respectively Subject Properties Fit to the Data Sample The relationship of each of the subject property s characteristics to the average of those of the sales can give an indication of where in the range of random variation the appraiser analyst would expect the subje
20. nd then rerun the regression based on the time adjusted sales prices Utilizing a trend of the adjusted sales price per property is much more accurate that one based on the unadjusted sales price or based on the adjusted sales price per square foot These new features are enhanced with reporting options 34 The emphasis of this module is to enable the user to perform these advanced techniques ina matter of just a few minutes This accomplished by programming these tools into the user interface rT Create a Regression Document Step 2 Scrub Data Calculate the number of days since the sale date Conn dct he Se Date v Date of Appraisal Monday Divide sales date into periods for Market Conditions Analysis MCA Column Indicating the Sale Date Date v Time interval span days 60 v Note the Dummy field set up in the scrubber Use both the Calculate number of days since sale and the Dummy field set up You can decide which one to use later 35 iables from the list on the left and move ight that those fields will play Descriptive Columns The identity of the property such as the address Dependant Variable Usually the property sale price Be sure to load the Independant Variables MCA Fields as an The fields on which to do the regression analysis 9 si independent variable 36 Note the Dummy fields will be set up automatically for you
21. oxville BETA TESTER Gustavo Mejido Gustavo is a State Certified Residential Appraiser in Miami FL Gustavo has extensive experience valuing residential properties including Single Family Residential Homes Condominium units Multi Family Residential 2 4 units and Vacant Land Gustavo s primary area of focus is on residential properties Especially those requiring significant research and market analysis In addition he has specific expertise in Valuation modeling Gustavo has taken several courses on regression analysis and statistics over the past several years and performed much self studies Regression Analysis has been an invaluable tool in my appraisal practice Using regression has greatly improved my market analysis ability to trend values over time estimate market values in a specific market area and as one gets more advanced to extract certain line item adjustments It has also given me more confidence to tackle more challenging appraisal assignments COMPONENTS 1 DEBAU 10 11 12 13 Import Feature a Directly from an open or unopened Excel workbook b Directly from most comma separated value csv files c Directly from the MS Clipboard Data Scrubber Loader loads the scrubbed data into the regress analysis Regression Engine A chart depicting the absolute error of each record in the data sample A remove outlier feature a Records can be marked excluding them from being removed b
22. play in currency format Number of whole places to round 0 Number of decimal places to display 2 ki gg Property Characteristics Other Factors Characteristic Value Other Factor Enter the Independent Variables for the subject property here Set the effective date of the apprajsal here Sample Oulput Value Display 123 456 79 Date of Appraisal Tuesday October 28 2008 p Value Comparable List Other Factors her added or subtracted from the regression analysis value This includes anything that effects value but was not considered in the data sample Some examples might be view lot size closing costs etc 23 REGRESSION SCREEN This Chart shows the percentage that the model missed actual sales price by 05 16 03 14 o0 oe 01 12 o 00 91 QP ee 02 9 oO 04 95 3 e 06 98 09 e e 11 02 a 0000 80000 90000 Selected Exempt No 7 a A N v v v v v v v vJ Select and deselect the sales oon Dom m amp w S D ba gt used in the analysis 100000 110000 120000 130000 BR Baths Age DaysSinceSale MCA120_1 3 2 17 691 1 0 4 2 17 6 1 0 3 2 16 661 1 0 4 2 20 646 1 0 1 1 16 631 0 3 2 16 616 1 0 4 2 19 601 1 0 q 1 23 586 0 1 3 2 15 571 0 1 Select the sales that you want exempted from the Remove Outlier button ooo oo 9 98 Oo oO 14
23. ression analysis in a format that is simple and easy for real estate professionals to use This product is suitable for variety of uses by most any real estate professional A partial list of users includes appraisers sales agents review appraisers mortgage lenders investors etc A partial list of uses includes extracting adjustments and predicting sales price rent rates capitalization rates etc The purpose of this user manual is to provide instructions on using the Regression application It is not intended to teach the theory of Regression Analysis or how to best perform such an analysis DEVELOPER David A Braun MAI SRA David has been appraising real property since 1976 During this period assignments have been performed on most all types of properties ranging from residential lots to complex commercial properties David s appraisal company reached over 20 employees He founded AVT in 1997 He is currently an approved instructor for the Appraisal Institute and is certified as a USPAP instructor by the Appraisal Foundation He has published articles on a variety of appraisal subjects in the Real Estate Valuation Magazine The Working RE Magazine The Live Valuation Magazine and the Appraisal Journal He authored the book APPRAISING IN THE NEW MILLENNIUM Due Diligence amp Scope of Work David Earned a Bachelor of Science in Business Administration with a major in Corporate Finance from the University of Tennessee in Kn
24. rmal data set The Over all Confidence Rating In the end each of the parameters mentioned has an effect on the expected accuracy of the prediction of the subject property s value In reality it is not just the size of each of these parameters which affect the appraiser s value opinion but the interaction of each of these on each other This check list is a good start to organizing the indicators of the predictive power of the market model on the specific subject property but a seasoned analysis will have developed an intuitive feel for this data If the appraiser feels the market model will predict the subject property s value within a range of 95 to 100 then it should have an over all rating of Excellent for a range of 90 to lt 95 then use Good and for a range of 85 to lt 90 then rate as Acceptable 30 PRINT amp SAVE New Valuation Ctrl h Ctri 0 Open Valuation Save Valuation Ctrl s Save Valuation As Copy Report Print Report Print Preview Page Setup Selected Ecempt No Sales Pice GLA Ctrl x 150000 31 STEPS TO PERFORM A REGRESSION ANALYSIS Be sure to watch the three part video and work through the Case Studies which are found on the Regression CD Collect the data Organize the data on an Excel Spreadsheet 3 Identify and make a note of the columns that will utilize the Regression s scrubber system a Days since sale DSS must be used instea
25. rofessional Landscaping etc all in one field m Features B Patio E Porch Covered L Insulated Windows Q Professional L 1 Fenced Yard 3 Screened Porch D Porch Enclosed G Wood Fe 2 Sunroom 3 Screened Porch C Deck E Porch Covered L Insul This is a very special situation which some MLS systems use and some do not In the example above the delimiter is the comma However many characters could be used to separate the entries such as colon semicolon dash slash pipe etc The scrubber in Regression Plus will try to determine the correct delimiter and separate the entries into individual fields Once that is complete it will construct a page with the data fields on the left side and a table of the individual fields with check boxes Field Value Fenced Yard 2 Sunroom 3 Screened Porch D Porch Enclosed E Porch Covered G W Fenced Yard 2 Sunroom B Patio C Deck E Porch Covered G Wood Fence K Storm Fenced Yard 2 Sunroom B Patio E Porch Covered G Wood Fence L Insulated Windo Fenced Yard 2 Sunroom B Patio E Porch Covered G Wood Fence L Insulated Windo Fenced Yard 3 Screened Porch B Patio D Porch Enclosed E Porch Covered Swim Fenced Yard 3 Screened Porch C Deck E Porch Covered G Wood Fence l Swimming Fenced Yard 3 Screened Porch C Deck G Wood Fence J Swimming Pool Above Groum A M M r o r t 19 1 Fenced Zz 3 Scr
26. st The Confidence Interval setting can be set from 80 to 98 This affects the Cls for each variable The Coefficient is the model s output for the characteristic The Weight is the weight that each of the variables has in the model The SubDev is a measure of how similar each subject variable is to those in the model The P value is a measure of the likely hood that the variable does not have a linear relationship to value The Std Err is the standard error for each variable No color coding is set for this as its impact is relational to the size of the coefficient 25 To begin the trend analysis Remove Outlyer Valuation Settings Set Confidence Rati The user should explore why any straight line varies more than about 3 5 in accuracy The polynomial feature may help in the analysis SyAAAAAAAAAA RARER ARAAAAAAAAILAAAAAAAARIAAAAAAAAARAAAAAAAAARAAAAARAARAAAARAAAARAARAAAAAARRAAAAAARARSIAAAAAAAARAAAAAAAAAAAAAAAARAARAAAAAAAAAAARARASARARAAAAAS L Trend Analysis between the sample This chart plots the selected Independant Variable against the price difference a trend in the accuracy of properties and their predicted model value The resulting best fit line may shof the model in relation to the variable chosen O GLA O BR DSS O Baths O Age Plot the price diff as Best Fit Degree Close Absolute O Signed A Sl 26 CONFIDENCE RATING CHECK LIST 1 Number of
27. t 1 0 but within the observation range Red Outside the observation range IMPORT FEATURE You can load the Regression two ways 1 From File In this method you will BB Crente a Repeat eine browse for the file containing the data It can be an Excel file or most Step 1 Load Data comma separated values CSV formats Z From File From Clipboard 7 l 2 Paste form the Clipboard In this method you would copy the data Data Loaded directly from Excel and then paste Street Number Street Name E a ET the data into the Regression gt 1203 Edinburgh Dr Maryville 37803 0 5 EDINBURGH Maryville 37803 1 The comparable data list must be scrubbed id ch E el 1217 N Wingate Way Maryville 37803 0 5 before it can be analyzed by a regression program Scrubbing data is the process of converting individual pieces of data to a form and format that can be analyzed Scrubbing can include omitting properties records entirely and omitting individual fields which are not pertinent to the analysis at hand It also includes formatting and restructuring of the data fields Each type of analysis will require different data formats and fields Scrubbing data is a new task for most appraisers The analysis that the data is intended for determines how the data should be scrubbed In a build up analysis method such as regression all of the data must be converted to a number format This is because regression analysis is h
28. this is not the case then dummy fields will have to be used Data Entry i In this case Blank Fields and above Ground entries will be marked Blank Field 0 for no and the entries for Gunite and In Ground will be marked 1 for yes 17 Scrubbing Feature 12 Create Dummy Fields This feature is found on the Step 3 Scrub Data Some More drop down It is used to create dummy fields where there is only one entry in each field For example fields for Subdivision where each datum can only be a single subdivision name In the situation below there are 33 records listing Allenbrook 1 listing Allenbrook Phase 2 2 listing Raulston and 20 listing Raulston View C Subdivision L Add Field X Remove Field je Rename Field Field Value a a nae Allenbrook Allenbrook o C Allenbrook Phase 2 1 C e C Raulston Z e C Raulston View 20 s e r In this case two dummy fields Allenbrook and Raulston View will be created with 0 posted for no and 1 for yes The original field will be removed 18 Scrubbing Feature 13 Separate Delimited Fields This feature is found on the Step 3 Scrub Data Some More drop down It is used to separate datum from a field where multiple entries are made in the same field see illustration below for Exterior Features Notice how the first record identifies a patio covered porch insulated windows P
29. traight line analysis tool The market condition Value adjustment MCVA that has resulted in many areas of the Country since the Real Estate Crash of 2007 is a good example of such a property characteristic The new Market Conditions Analysis module is designed to provide additional tools based on multi linear regression analysis to aid the valuation professional in meeting the challenges in market condition changes other than a constant straight line scenario Y axis Value Trend 204 23 7 184 23 sss Trend Line 144 23 124 23 104 23 84 23 The above chart plots the sales price per square foot over time Notice that the blue line tends to fit the data better than the straight red line In a situation like this an analysis based on a Straight line may not return accurate results 33 The new Market Condition Analysis Module is designed to help you perform the following e Extract the market s value trend line based on the adjusted sales price using dummy fields e t will create both a value trend line and a volume trend line e Utilize the trend line to o Determine if a straight line analysis is appropriate for the market condition trend Form an opinion concerning the direction of the value trend Make the appropriate market condition adjustments to the comparable sales used in the direct comparison approach o Adjust all of the sales in the regression analysis for market conditions a
30. ve a name for the new field Then select the fields you wish to combine by double clicking on the appropriate field names Add spaces commas etc so that the syntax is appropriate i a ic Column Combination mes Combine Columns Available Columns You may combine several columns into a single column either Street Number a by concatenating them or by using a mathematical expression Street Name Double click a column name in the list of available columns in Town order to add itto the expression Zip Land acr Combination Type fe Mathematical Allenbrook A Texuai Raulston View New Column Name Address ee t Expression Street Number Street Name Town In this example the new field will be named Address An entry for the new field might be 1221 Brookhaven Drive Knoxville The three original fields will be removed and not be brought forward to the regression analysis 13 Scrubbing Feature 7 Combine Fields based on Mathematical Operations This feature is found on the Step 2 Scrub Data drop down To access this feature select the Add New Combination button On the drop down select the fx Mathematical Button and give a name for the new field Then select the fields you wish to combine by double clicking on the appropriate field names Add the appropriate mathematical operator to direct the calculation Column Combination Com
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