Using GIS To Improve The Services Of A Real Estate Company
Using GIS to Improve the Services of a Real Estate CompanyKevin H. Donlon¹ ׳ ²¹Department of Resource Analysis, Saint Mary’s University of Minnesota, Winona, MN55987; ²Shane P. Donlon, Inc., Patterson, CA 95363Keywords: Real Estate, Geographic Information Science (GIS), CaliforniaAbstractA picture may be worth a thousand words but a map tells a story. It speaks to the viewerby exposing its many relationships. Its testimony is unquestioned making it a powerfullypersuasive tool. This report will discuss how this tool will be used by a real estatecompany to improve its services, woo prospective clientele, and ultimately contribute tothe company’s bottom line. A Geographic Information System (GIS) captures, analyzes,and displays data in a visual, spatial context. In essence, the product of a GIS is a map.Use of GIS technology is particularly fitting to the application of real estate practiceconsidering that property is geospatial in nature, its associated attributes are plentiful, andthe relevance of location is key. This paper describes how a large map was producedusing GIS technology. The map depicts over 1,000 properties displaying the propertyowner’s last name, assessor’s parcel number, acreage, Williamson Act status, and itindicates which properties have been sold by Shane P. Donlon, Incorporated. The paperdescribes how GIS can use prior sales data to illustrate current market trends and createcustomized maps for market perception. The results will aid investors so that they maybe well informed while contemplating expensive decisions.erratic for a real estate agent. The roleof GIS amongst the instability of realestate possibilities is to supply visual,factual evidence to its users therebyproviding quality service.Real estate transactions do notflow alike. The people factor alonethreatens the integrity of an agreementwhen dealing with the emotional ups anddowns associated with buying andselling property. Suppose a GIS couldproduce a map that afforded peace ofmind to a principle about his or herdecision. Effectively, a client’sIntroductionThe nature of real estate brokerage is toarrange an agreement between theparties involved and administer itsconsummation. For this service, acommission is paid when a titlecompany delivers title and distributesfunds. So how does a real estatecompany get itself into a position wherea title company ultimately writes it acheck? With neighborhood rumorsabout a surreal market tantalizing buyersand sellers, payday can be frighteninglyDonlon, Kevin. 2007. Using GIS to Improve the Services of a Real Estate Company. Volume 10, Papersin Resource Analysis. 11 pp. Saint Mary’s University of Minnesota Central Services Press. Winona, MN,Retrieved (date) from http://www.gis.smumn.edu1
200 for one shapefile containinglimited attribute information. ChicagoTitle Company’s customer servicedepartment provided supplementarytabular data gratis. Stanislaus Countydelivered the shapefiles for free butcharged 570 for three years of salesdata in TAB delimited text files. MercedCounty charged 1,000 for GIS data inmultiple formats.Data acquired from thesecounties included: roads, creeks, canals,rivers, railroads, zoning, cities, lakes,and parcels. Although much datacontributed to this project, the parcelsshapefiles were the spatial foundation.The parcels shapefiles contained thevisual representation of propertyboundaries and its attributedinformation. One attribute ofsignificance was the assessor’s parcelnumber (APN), which is a referencenumber commonly used to identify aparcel. These numbers were used toconnect tables for join operations inArcMap.Other data utilized for thisproject came from the public land surveysystem, the multiple listing service(MLS), and Shane P. Donlon, Inc. Thepublic land survey system providedsection line data for free online. Themultiple listing service contributed salesand listing data. A membership fee of 140 per quarter is extended to licensedreal estate agents. Finally, sales datafrom Shane P. Donlon, Inc. was used fordisplay of in-house sales.insecurities would be appeased. Thepersuasive capability of an omniscientmap could combat remorse andconsequently solidify a pendingagreement.While maintaining the sanity of aclient throughout a potential transactionis vital, winning a client’s trust in thefirst place is important too. Clients aregained by various methods. Arecommendation from a previouslysatisfied customer is probably the mostsecure. However, the focus for theproduction of the large wall map was totarget new clientele that may beshopping for a real estate agent. Theobjective is to impress that individualand subtly solicit the birth of a fiduciaryrelationship.GIS CartographySoftware UsedAlthough much time was spent usingMicrosoft Excel and Access, the drivingsoftware for spatial data operation wasArcGIS, developed by a company calledEnvironmental Systems ResearchInstitute (ESRI). ESRI is the leadingGIS software company in the industry(ESRI, 2007a).Data AcquisitionThe majority of data collected for thisproject were purchased from threecounty governments in California. Thecounties charged for the data becauseaccording to a provision in the CaliforniaPublic Records Act, a countygovernment may charge a fee for datamaintained in an electronic format aslong as the fee represents the cost ofproduction of the data (Lockyer, 2005).In this case, San Joaquin County chargedData ConfigurationUsing ArcGIS, the first objective was toestablish a geodatabase and subsequentfeature datasets for data consolidationand organization. ArcCatalog was usedto establish a common projected2
replace operation was executed whereinthe asterisks symbol was used torepresent any character or characters.With the focus on the find input box, )*was entered to be replaced by a no valueentry. The result left only the first wordof the original string value which wasalmost always the owner’s last name.The data was then ready to be joined inArcMap.coordinate system for all spatial datawhich were then appropriately groupedas feature classes according to category.In ArcMap, the data werereduced in size for efficient processing.Only the study area from the featureclasses were selected and exported to aworking geodatabase. The tabular data,however, were not easily manipulated.Stanislaus County, for example, sentfour TAB delimited text files with162,629 total records. The data wereopened in Microsoft Excel and Accessso that it could be edited for a one to onejoin to the parcels attribute table. TheAPN columns from each table needed tomatch exactly in order for the join to besuccessful. But the APN field in theparcels feature class was formatted as astring (000-000-000) and the data fromthe TAB delimited text file was read as anumber in Microsoft Excel withouthyphens. In Excel, hyphens were addedwith a custom expression using theformat cells tool. The data type wasthen converted to a string forequivalency.Also in Excel, another text fieldwas manipulated in preparation for theupcoming map display. A columndisplaying the property owner’s fullname in upper case letters needed to bealtered. A new column was created withthe desired outcome of populating it withthe owner’s last name only in lower caseletters, except for the first letter. Toeliminate every other word in the stringother that the first word, the find andreplace method was used with acharacter code. First, all spaces in thecolumn were replaced with the endparenthesis “)” key. This key waschosen because in no situation could thiskey casually appear thereby spoiling theoutcome of the operation for even onerecord. Secondly, another find andSymbologyIn the beginning, raw, unpolished datapopulated ArcMap according to randomassignments and default settings. Sincethe objective was to print a map fordisplay, each layer was meticulouslysymbolized for logical, realisticrepresentation. The Zoning layer wasunique because it was categorized intomultiple divisions according to specificdescriptions in a field. Once each layerwas satisfactorily displayed, the featureswere ready to be labeled and convertedto annotation layers.Labeling and AnnotationAdditional skills were required to fulfillthe task of specialized labeling in themap. ESRI’s virtual campus (ESRI,2007b) offered two courses entitled,Labeling in ArcMap: Tips and Tricks,and Creating and editing Labels andAnnotation. The courses were detailedand even provided sample code foradvanced labeling. The programminglanguage used in the courses and inArcMap was VB Script. A sample ofcode was harvested from the Labeling inArcMap: Tips and Tricks course andmodified to fit the needs of this project.The code converts all uppercase text in afield to all lower case text except for thefirst letter of each word. For example,3
instead of labeling ‘VINEYARD AVE’,the code converted it to‘Vineyard Ave’,a more appealing display. The codeused for this operation is found below:labeling instructions were coded to usethe county’s values unless it was a “0” inwhich case the shape area value wouldbe substituted. To distinguish thedifference on the map display, countyvalues were concatenated with ‘Ac.’ andthe few shape area values needed wereconcatenated with ‘AC’. Figure 1 showsan example of the resulting difference.Function FindLabel ( [STNAME] )Dim t, newS, it [STNAME]newS UCase(Mid(t, 1, 1)) 'Upcase 1st chari 2Do While i Len(t)' check if previous char is a spaceIf InStr(1, Mid(t, i - 1, 1), " ",vbTextCompare) 0 Then ' space foundnewS newS & UCase(Mid(t, i, 1))ElsenewS newS & LCase(Mid(t, i, 1))End Ifi i 1LoopFigure 1. The label on the left shows the acreagecalculated by ArcMap and the label on the rightis the acreage on record at the county.The code below was used to make thispossible:FindLabel newSEnd FunctionFunction FindLabel([Join Me Stan OwnerShort],[Update Colony APN],[AssAcres ASSACRES],[Update Colony EST ACRES])The same outcome could also beaccomplished in Microsoft Excel usingthe PROPER function; however, sincethe quantity of data did not alwayscooperate with Excel, it was easier toutilize ArcGIS to debug the code above.If ([AssAcres ASSACRES] 0) thenFindLabel [Join Me Stan OwnerShort] &VBNewLine & [Update Colony APN] &VBNewLine & [AssAcres ASSACRES] & "Ac."Labeling SolutionsData obtained from Stanislaus Countywere incomplete. For example, one ofthe desired labeling outputs was acreagebut the acreage field from the county’sTAB delimited text file had some zerovalues instead of the actual acreage onrecord. Ideally, the acreage displayed onthe map would be consistent with thatfigure on record at the county, but a “0”value display for a property’s area makesno sense. ArcMap contains a shape areafield automatically created at the birth ofa polygon shapefile. The values wereeasily converted to acreage using thefield calculator supplying two, slightlydifferent values for acreage. TheElseFindLabel [Join Me Stan OwnerShort] &VBNewLine & [Update Colony APN] &VBNewLine & [Update Colony EST ACRES]& " AC"End ifEnd FunctionSQL in LabelingTo avoid labeling polygons in the datasetthat were of no significance, such asround-a-bouts and streets, a structured4
query language (SQL) statement wasused as shown below, which eliminatesall untaxed polygons:has sold a lot of property. The intendedmessage is that Shane P. Donlon, Inc. isthe most experienced, local real estatecompany. Figure 2 below displays aregularly labeled parcel and one sold byShane P. Donlon, Inc. for viewerdistinction:[Update Colony APN] ''This simple statement includes onlythose parcels with an existing APN.Labels to AnnotationAnnotation stores text or graphics. Itcan be saved as a separate feature classand edited independently. Newannotation layers were created from eachlayer’s labels and stored in thegeodatabase as per the suggestion of theESRI Creating and editing Labels andAnnotation course. Using the annotationtool bar, the annotation layer could bequeried, individually modified andfeature linked. Feature linked annotationrequires an ArcInfo license (the highestlicense level) and maneuvers text tofollow the curve of a feature.Figure 2. Both labels display property attributes,but the label on the left also shows that theproperty has been sold by Shane P. Donlon Inc.as indicated by the colored text.Williamson ActThe California Land Conservation Actof 1965 is commonly referred to as theWilliamson Act, which enables localgovernments to enter into contracts withprivate landowners for the purpose ofrestricting that property to agriculturaluse. In return, landowners receiveproperty tax assessments which aremuch lower than normal because theyare based on farming and open spaceuses as opposed to full market value(California Conservation, 2007).Williamson Act status was used for mapdisplay. A Williamson Act column waspopulated with a Y or N indicatingwhether or not the property was or wasnot under the provisions of the landconservation contract. Another columncontained the expiration date if a noticeof non-renewal had been filed by theproperty owner. It was necessary towrite additional code to display ameaningful symbol and text to representthis information. Figure 3 shows anexample of how the symbol and text wasdisplayed on the map.Annotation SpecializationBy using a table of sales data fromShane P. Donlon, Inc., a new layer wascreated. The table listed the propertiesthat were sold by the company since itsinception. The table was joined to theparcels layer so that the sales data couldbe queried and selected. By exportingthose selected parcels, a new layer couldbe displayed depicting polygons thatrepresented property previously sold byShane P. Donlon, Inc.Subsequently, using the newlycreated layer, a selection by locationoperation was executed so that theannotation that was contained within thesold properties could be selected and resymbolized for a unique presentation onthe map. The desired effect was to showthe viewer that Shane P. Donlon, Inc.5
was developed and an 8½ by 11 printoutaided in the placement process. Sincethe map is 67 inches tall and 36 incheswide, eye level positions won elementsof higher priority. Two overview mapswere included in the layout forreference. Each overview map wasassembled in extra data frames inArcMap. One was of a large scaled mapof California identifying the subject areawhile the other data frame displayed aninsert from the California map foradditional reference. Data for these dataframes were collected from an ESRImedia kit wherein a layer file with presymbolized data was utilized.Figure 3. This symbol was used on the PattersonColony Map to indicate that a property wasunder the Williamson Act contract. A yearunderneath the symbol was displayed if thecontract was scheduled to expire.The cactus in figure 3 is actually theletter ‘Y’ in the font ESRI SDS 1.95 1.The ‘Y’ originated from the WilliamsonAct column signifying that the propertyis under the land conservation contract.Rather than label a plain ‘Y’ inside thepolygon, the cactus seemed appropriateconsidering its representation. The font,however, needed to be converted intolegible characters for display of theexpiration year. Below is the code usedto fulfill this operation:AnalysisRationaleSince real estate brokerage is a serviceindustry, its product is not tangible.Quality service in the business equates toknowledge, experience and reputation.Prudent clients want to know that theyare working with someone who is wellinformed about the area and its currentreal estate market trends. Although thequestion, “What is happening in the realestate market today?” is vague anddifficult to answer, customers want aresponse. Utilizing GIS, a real estateagent could have a few maps preparedfor such a moment. Figures 4, 5, and 6illustrate real estate sales for the lastthree years.The maps indicate activity hasslowed down dramatically in 2007. Inthe year of 2005, in Stanislaus County,14,372 properties were sold. In 2006,10,394 properties sold and for the firsthalf of 2007, only 470 properties havesold. The credibility of the maps aretrustworthy which is exactly how greatreal estate agents should be.Function FindLabel([Parcels.Update Colony WILLACT],[Parcels.Update Colony WILLYEAR],[Parcels.Update Colony WILLNONREN])If ([Parcels.Update Colony WILLYEAR] 0)ThenFindLabel [Parcels.Update Colony WILLACT] &VBNewLine & " FNT name 'Arial' size '9' "& [Parcels.Update Colony WILLNONREN] &" /FNT "End IfEnd FunctionLayoutFrom Science to ArtOnce the mechanics of fulfilling layerobjectives were achieved, the nextmilestone was to aesthetically displaythe map elements. A list of elements6
The next question that may beasked from potential customers is,“Why?” Although many factors swayreal estate activity, typically, as the costof money increases, real estate activitydecreases. Figure 7 is a graph showingthe relationship of one company’svariable loan pricing with time(Yosemite Farm Credit, 2007).Figure 4. Each dot represents a property sold in2005 in Patterson, California, Stanislaus County.Figure 5. Each dot represents a property sold in2006 in Patterson, California, Stanislaus County.Figure 7. ILP represents Yosemite FarmCredit’s Variable Loan Pricing which is basedupon the Libor Index.The MLS and GISResidential Real EstateFigure 6. Each dot represents a property soldthrough June of 2007 in Patterson, California,Stanislaus County.Much of a real estate agent’s life is spent7
aspect in a way that the MLS cannot.Suppose a client seeks a home locatedno further than one half mile away fromhis or her sister’s house. A GIS canoperate the same search as discussedearlier but include a distance parameterof any length from any point on earth.Using MLS data, a GIS canproduce a map for a client that couldreduce the mystic of house hunting,thereby calming apprehension. Figure 8illustrates an example of what a clientcould observe from a GIS.studying the matrix of the multiplelisting service (MLS). The (MLS)provides a network of real estateinformation to real estate brokers andagents in the Northern California CentralValley. Within the geographic servicearea, real estate professionals haveaccess to every listing and sale withinthe database (MetroList Services, Inc.,2007). For example, the informationabout the average price of a home thathad between 1,500 and 2,000 square feetsold in Patterson, California in 2004with at least 3 bedrooms and 2bathrooms closed at 316,011. In 2005,the average home under those samecriteria sold for 400,994. In 2006, thatsame average home sold for 413,334and in 2007 it went for 349,919. Thecurrent, average asking price for a homeunder those same criteria as ofSeptember 18, 2007 was 329,669.Since the current asking price for housesis about 5.8% lower than the sales pricesof generally comparable houses fromprevious year, it is reasonable toconclude that today, property values aredepreciating. Information availability ofthe MLS is impressive and invaluablyuseful for industry professionals.However, although the MLS allows for asearch by area, the selectable boundariesare broad in range, crude and confusingamongst MLS subscribers. Thecheckbox option for the area of Pattersonencompasses approximately 72 squaremiles. Since the relevance of location iskey for clientele, the introduction of GISseems idyllic. While logged in to theMLS, a vast amount of data can beexported into a .CSV file, opened inMicrosoft Excel and added to ArcMap.The very same analyses can beperformed by ArcGIS as with the MLS.But the GIS can incorporate a spatialAgricultural Real EstateAgricultural real estate differs fromurban, residential real estate in manyrespects. Agricultural real estate usuallycosts more, has more land, andsometimes does not even come with ahome. In a suburb, neighboringproperties are relatively similar, whereasin the country, neighboring propertiescan be very different from one another.Figure 9 conveys an example of howagricultural property can be displayedand labeled using GIS. Sinceneighboring properties in the county candiffer greatly, they are indistinctlycomparable. Nevertheless, if a prudentbuyer was to consider purchasing one ofthose green polygons, he or she wouldwant to know about the red ones.DiscussionThe Bottom LineThe underlying, pressing questionthroughout the development of thisproject was, “How can GIS make moneyfor a real estate company?” After all,the software does not arrange meetings,negotiate contractual terms, or even8
Figure 8. Using ArcMap, a visual understanding of neighborhood real estate activity is gained. The GreenParcels represent properties that are for sale. The red parcels show those properties that have sold.Figure 9. Some of the property’s attributes have been labeled for a quick, visual comparison.9
Office Suite to 2007. The differencebetween the two ArcGIS grades wasminor, but Microsoft 2007 was superiorthan 2003 because the newer version ofExcel allows for 1,048,576 records ofdata input. Microsoft Excel 2003 onlypermits 65,536 records. Since theparcels layer for Stanislaus Countycontains 162,629 records, datamanagement was challenging.ArcGIS 9.1 and 9.2 struggled todisplay even one county of parcel data.The three counties together containedabout 500,000 polygons so adding themall to the map was unreasonable. Thedata had to be clipped into manageablesizes to keep ArcMap from continuouslycrashing.Another time consuming aspectto the project was updating the salesspreadsheets obtained from Shane P.Donlon, Inc. The sales data acquiredfrom the real estate company wasentered over a period of 10 years. Sincethen, many of the current APNs havechanged. Independent investigation wasrequired to reconcile the salesspreadsheets with ArcMap’s attributetable so a one to one join would besuccessful.smile.It can, however, serve a realestate company in the arduous process ofachieving buyer and seller acceptance.If even one client is procured as a resultof the use of this technology which leadsto a transaction that is consummated, theinvestment of GIS will have paid foritself.Today and TomorrowReal estate entrepreneurs would be veryinterested to know if GIS could forecastreal estate values. GIS analysis,however, is only as precise as the dataprovided. Some details about real estateare not recorded. For example, supposetwo new homes next to each other ofequal size and design sell on the sameday for the same price. One year later,comparable sales data may indicate thatthose two homes are worth the same.But if one owner added a cement patioand a hot tub while the other poorlymaintained his or her residence, thatdifference would not be reflected in adata analysis of property values.Nevertheless, as technology continues toadvance, data acquisition, storage andmanipulation become easier and lessexpensive. The identification of realestate attributes is becoming moredetailed. As the precision of this dataimproves, so will the reliability of GISanalysis.As for now, the threat of realestate market uncertainty will continueto haunt investors.AcknowledgementsThis project was made possible by thequality education and support bestowedby the Department of Resource Analysisat Saint Mary’s University of Minnesotaand Shane P. Donlon, Inc. Individualrecognition is deserved by PatrickThorsell, John Ebert and DavidMcConville, PhD of Saint Mary’sUniversity, and Karin Pettit and Shane P.Donlon of Shane P. Donlon, Inc.TroubleshootingArcGIS 9.1 and Microsoft Office Suite2003 were used at the beginning of thisproject, but by its completion, ArcGISwas upgraded to 9.2 and MicrosoftReferences10
California Conservation. 2007.Williamson Act Program. Division ofLand Resource Protection, RetrievedAugust 10, 2007 from http://www.consrv.ca.gov/DLRP/lca/.Environmental Systems ResearchInstitute, Inc., 2007a. About ESRI,General Information. Retrieved July2007 from http://www.esri.com/about esri.htmlEnvironmental Systems ResearchInstitute, Inc., 2007b. My VirtualCampus Courses: Labeling in ArcMap:Tips and Tricks, Creating and EditingLabels and Annotation. Retrieved July2007 from /index.cfLockyer, B. 2005. Opinion of BillLockyer, Attorney General Office ofthe Attorney General No. 04-0115.Retrieved: July 12, 2007 List Services, Inc. 2007. MLSData Export (Sep 18, 2007),Sacramento, CA. Retrieved fromhttp://www.prospector.metrolist.netYosemite Farm Credit. 2007. Copy ofHistorical Rate comparison graphs forcustomers. Received Jul 26, 2007 via email from Elizabeth Piersante.11
Sep 18, 2007 · system, the multiple listing service (MLS), and Shane P. Donlon, Inc. The public land survey system provided section line data for free online. The multiple listing service contributed sales and listing data. A membership fee of 140 per quarter is extended to licensed real estate agents. Finally, sales data from Shane P. Donlon, Inc. was used for
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