Case Studies and White Paper: Real Estate Industry

Subject: Case Studies
Pages: 9
Words: 2492
Reading time:
10 min
Study level: PhD


The Geographic Information system is a new technology whose invention heralded a new dawn for the real estate industry. Problem-solving in the real estate industry is especially a big problem as it is often known to be complex, uncertain and dynamic (Wofford 8). In the traditional way of doing things, solving problems in real estate often involved deductive reasoning and decision making. However, a company that has embraced the GIS will not have to use these complex and tiring methods. Incorporating the GIS in decision-making steps and prediction simplifies the process to one achieved by a mere click of the button. Real estate analysts who can access the GIS packages can perform complex spatial analysis. They can also prepare maps in seconds thus making it extremely cost-effective (Fryrear 4). Their colleagues, who have no clue about this technology, will take some time conducting similar analyses. A good GIS system can process geographic data from as many sources as possible (Robbins 56). A GIS, which is up to date, may come with extra packages. This may include maps, graphic representations and allow zooming in all angles. The databases should also not be limited to the ones from public or government institutions. The use of global positioning system is another plus that allows maps to have coordinates.

The GIS in real estate

Real estate companies in the US are continually using GIS technology in their prediction analysis. Trulia has developed a new technology that helps them determine the estimates of US homes in the short-run (O’Connell 6). The technology is aimed at providing leading indicators on prices of homes, as well as rent in the US. The technology also helps them determine the trends in rent prices for houses in the same areas or similar localities. On the purchase of homes, the technology can predict the prices for a few months down the road for homes in a given city (Shankar 89). Some of the reasons that make the GIS system a catch in the industry include the reduction in price of the product since its invention in the mid-nineties. Another factor is that computer technology has improved. Computers now have fast processing speeds, bigger memory, and more computing power (Zeng & Zhou 309). To process the large amounts of data in a GIS, power and speed are important. Computers have become easy to use thus not limiting the user on account of user-friendliness. The third factor is the rise in the number of engineers who are developing software for use in all kinds of business. Finally, there has been an increase in the availability of data, especially from private vendors who sell this data at lower costs. The government and state agencies are also displaying considerable geographical data (Simons 6). The real estate industry is one industry whose full adoption of this technology.

The location of a certain property is always very important. Before the introduction of the Geo-based technology, doing certain tasks was not possible. To date, players in the real estate industry who have ignored this technology operate in a manual world and lose a lot. Today’s GIS systems are automated thus perform spatial processes automatically (Abukhater 2). The distance from a house to the nearest metropolitan area has often been used as a guideline in understanding the effects of various attributes on the price of houses. Traditionally, the straight-line distance from the house to the metropolis has been used as a proxy for transport costs. GIS can easily produce a variable that identifies all properties that border a certain property that meets certain criteria. To make such identification would take days and cost a lot in terms of labor. The other major technology under GIS is the predictive analysis. This is a technology meant to give an insight or foresee market trends, identify risks, and balance the location strategy. Predictive analysis may play a critical role in the determination of the best investment for a player in the real estate market. This is done by identifying future potential areas. This technology is statistical, and it involves getting information from different sources and using the information to foresee and predict future trends. If GIS was to be combined with all information concerning land, it would produce an array of information affecting the property, environmental state of the land, and the level of preference (Heppleston 4). If we can know what will happen in the future, we can do something today. One of the advantages such a model would give is the ability to preserve the environment. For example, one can ensure the new building coming up can maximize the use of renewable energy sources. Buildings should produce efficient energy. The building should also have access to a transport system. In addition, the house structure should help in the preservation of water. The real estate expert would also evaluate waste disposal techniques so that the property’s future desirability is not destroyed by the dumpsites. All these analyses are based on the demographic and census figures suited for the wishes of the client. Other than the risks from the environment, this technology helps investors mitigate the financial risks (Von 7). With a good insight, investors can tell which areas are showing hope, and those that are bad for business.

The use of the GIS system in the real estate industry presents a clear break from the past. It also offers a path into the future away from the traditional manual systems that often cause the buyer stress and uncertainty. Local-Insights have developed this phenomenal system which, if funded, will provide immense benefits as the future approaches. The fact that the method can codify expert knowledge mitigates the risk of lacking data in the industry. Thus, the method is convenient for the real estate brokers and homeowners. In addition, it provides an opportunity for buyers to get value for their money without unnecessary fatigue.

Case Studies

John had just finished law school and landed a job as a lawyer in one of the corporate firms in Washington DC. The company offered a good salary, with a car and a mortgage on top. The student loans were no longer bothering him, and one day, he decided it was time to buy a house. With a $ 200,000 mortgage, he was sure to get a pretty decent house, and he preferred a house in a quiet neighborhood. Given the huge number of hours that the lawyer was required to be in the office, he hired a real estate agent to help him with the search. After explaining to him the details of his house, he paid a fee of $100 dollars with a promise of another $500 once they found a house. Three weeks later, he had only had two options. Both options did not have what John was looking for; either being way beyond his loan or simply in the wrong neighborhood. It was then that he decided to try online searches. On one of the websites, he signed in, but another three weeks passed without any communication. It was while he was surfing for other options that he found out about the Geo-based technology. After his call, we booked him for an appointment during which we explained to him how we usually carry out our activities. We logged his data, and in less than two minutes, John had a choice of four houses that fit his choice. In no time, John had moved into the house as one of the houses pleased him and was well below what he had averaged!

Adam Clay had just returned from China where he had worked as an executive in one of the firms. He had returned to the United States with the aim of investing in the real estate industry. He had approached a real estate agent recommended by his bank. Anxious about new prospects, he hurriedly booked an appointment. The agency informed him that they would need at least a week to send messages to their agents in the field. Later, while reading the business paper, he came across a story of an investor who was lamenting about the lack of data on the prospects of different markets. The paper had also included an analyst’s opinion who had recommended the Geo-based system. Clay called Local-Insights. At Local Insights, all we had to do is log in to the local area with the highest activity to attract real estate investors. Within seconds, we had the information that we needed. Clay was able to identify the local area that had the best prospects in the next two years. In addition, we were also able to locate the top 10 market closures with a mere click of the button.

Home Brokers is a real estate agency in Colorado. The company has been experiencing difficulties as the cost of living has risen and paying agents has become expensive. The company decided to increase the commission to meet the operational costs. However, the company has been having difficulty getting new business. With the use of a Geo-based search system, the company has realized it will no longer need as many agents as it has had.

Truck-Movers have been growing in the last three years and need to relocate to more expansive premises. The company has hired agents to look for a convenient property, but the property they are getting is way above the rates in the market, and they cannot afford it. While the operations manager is surfing for alternatives, he learns of Geo-based information used to help real estate users locate property that is suitable for their business. The company decides to contact Local-Insights. It takes a few seconds and though the price is a little bit higher than the former agents, the company manages to relocate in one week.

The Housing and Décor Company engages in the construction of houses for sale. The company would like to know the best places to buy a property to develop luxury apartments. However, the only Geo-based information they are getting does not provide a predictive model. It also limits its data to the one available in the public domain, and the data is not visualized. Local-Insights have developed a new product that provides all these details.

White Paper

In the real estate industry, value and potential of the property are mainly determined by the location. GIS will aid in the making of such decisions. For home buyers, this system can help them determine where to buy property. When a homeowner decides to buy a house or any other property, several questions come to mind. This includes the question of where to buy and the house that one would prefer. In the former question, the buyer is wondering about the location. The latter question refers to the taste and preference that the customer has in mind. In terms of location, the determinant of this would be where the customer works, the income, and proximity to a metropolitan area. A good property that suits the buyer should be affordable, or in the range of current market prices. It should also have positive prospects of gaining capital. As discussed before, the location is another key determinant. In fact, it is the dominating factor. To determine a good location, one can use a Geo-based system. The real estate Geoinformation system (REGIS) performs two key roles: it analyses the physical and social environment in the potential area and mapping it. In determining a prime location, there are several basic factors that should be considered. One is the environment. Is it a slope? How is the vegetation? How are the parks? Is there a river? Are there dump sites that can cause pollution? These questions define the environment. The other determinant is the social environment. These include the availability of shops/malls, railway stations, institutions of learning, noise from industries, population, and price of homes. The other important factor is the personal factors. These include the client’s income and budget or the size of the housing loan, the place of work on the client, location of friends and family, the size of the client’s household such as staff and kids, and the client’s preference in population groups.

The GIS can be incorporated so that it has tools to analyze physical and social factors and establish maps that point to the strategic areas. The system uses complex algorithms and fuzzy theories and once completed. They are changed systematically to become guidelines that can be used in filtering out areas that meet the client’s expectations. Optimization is then carried out. In a nutshell, this system takes into account the major factors: personal preference, physical environment, and social factors (Fryrear, Prill and Worzala, 155). This method when cross-checked is in the range of up to 97% accuracy. If a buyer were to use the traditional way of doing things, searching for this property would take weeks and cost the client time, money, and the stress that comes with such a process. It is also important to note that buyers are largely inexperienced in the world of real estate. Hence, they would have limited knowledge, or none at all in terms of determining the effect of the physical environment on property value. This method, on top of being fast also helps the buyer to get expert knowledge and avoid potential mishaps. The method has another major advantage in that it can process both incomplete and non-defined criteria. Such a criterion may include expert knowledge and obvious knowledge. In this case, buyers who fail to use the method will suffer from lack of data. The property price is a factor that should also be noted. Through a model known as the Automatic Valuation model that is incorporated in the Real Estate Geo-Information System, one can tell if a property is worth the asking price. The Automatic Valuation model is analyzed through mathematical and statistical methods. Later on, the model then compares the values of property based on the similarity in the attributes of the property. These attributes may include the age of the house or the number of rooms.

However, this technology is not limited to buyers. Real estate investors who come to Local-Insights are assured of the best predictions in the next one to two years. By monitoring local area action, we can tell which markets are best to put investment. The same applies to real estate agents. We can put local area action on your website. This will enable you to know areas where business is good and save on operational costs.


From the discussion, one can learn that the Local-Insights presents the most accurate and real-time market information in the real estate industry. We have critically developed our technology, and the software is one of the best. With these tools, we can do the best analysis in the market. Our technology has no limits of the country, state, or county as we will go down to block you desire.

Works Cited

Abukhater, Ahmed. GIS best practices. Austin, Texas: University of Texas press, 2008. Print.

Fryrear, Richard. The Use of Geographic Information estate geographical information system (REGIS). 2000. Web.

Fryrear, Ron, Ed Prill and Elaine M. Worzala. “The Use of geographic Information Systems by Corporate Real Estate Executives”, Journal of Real Estate Research, 22. 1⁄2(2001): 153-164. Print.

Heppleston, Alex. The Spatial Dimensions of Multi-Criteria Evaluation – Case Study of a Home Buyer’s Spatial Decision Support System. 2006. Web.

O’Connell, Brian. Trulia Reveals ‘Crystal Ball’ Home Price Predictor. 2012. Web.

Robbins, Martin. Spatial Decision Support Systems for Automated Residential Property. 1996. Web.

Shankar, Pravin. Crowds replace Experts: Building Better Location-based Services using Mobile Social Network Interactions. 2003. Web.

Simons, Robert. Using GIS to make parcel-based real estate decisions for local government. 2001. Web.

Von, Meyer N. Gis and Land Records: The Arcgis Parcel Data Model. Redlands, Calif: ESRI Press, 2004. Print.

Wofford, Larry. Real Estate Problem Solving and Geographical Information System: A stage model of Reasoning. 1997. Web.

Zeng, Thomas Q. & Qiming Zhou. “Optimal spatial decision making using GIS: a prototype of a real estate geographical information system (REGIS)”, International Journal of Geographical Information Science, 15.4 (2001): 307-321. Print.