Understanding Probability in Real Estate: A Case Study on Township 3

DB Week #3 Probability is defined by Lind et al. (2019) as “a value between zero and one, inclusive, describing the relative possibility (chance or likelihood) an event will occur” (p.119). In this example, there are 25 houses located in Township three, 18 of which contain a pool. Therefore, I determined that the probability of a house located in Township three and containing a pool was 18/25 or 72%. Next, I used the joint probability method to determine the probability that a home is in Township three and contains a pool. Joint probability “measures the likelihood two or more events will happen concurrently” (Lind et al., 2019, p. 130). I first determined the probability of a house being located in Township three, which came out to 25/105 or 23.8%. Next, I determined the probability of a house containing a pool, which was 67/105 or 63.8%. Finally, to determine the joint probability of the two variables, I multiplied .24 by .64. I concluded there is a 15.4% probability that a home is located in Township three and contains a swimming pool. Type Probability Explanation Township 3 p(a) = 25/105 25 of the properties are located in Township 3. Pool p(b) = 67/105 67 of the properties contain a pool. Joint probability p(a and b) = .154 The probability of a property being located in Township 3 and containing a pool is approximately 15.4% Realtors can use the information on the probability of a house located in Township three containing a pool to assist and best serve their clients. The realtors of the estate can easily find information about the current properties available through probability. An example of this use of

probability is determining how many homes in Township three contain swimming pools and in this example that is 18. Then, they can place their clients in homes where they fulfill their needs and wants. Galatians 5:22 says, “But the fruit of the Spirit is love, joy, peace, patience, kindness, goodness, faithfulness, gentleness, self-control …” (Eastern Standard Version). The realtors should use these attributes when serving their clients as to please not only the clients themselves but the Lord. As Christians, we work for the Lord by serving our neighbors. Another verse that explains this concept is Galatians 6:9-10, which states, “Be kind to one another, tenderhearted, forgiving one another, as God in Christ forgave you” (ESV). One probability used frequently is the likeliness of getting into an accident determining car insurance rate. To determine this rate, insurance companies use “gender, age, type of car and number of kilometers driven each year to estimate an individual person’s probability (or risk) of a motor vehicle accident” (Institute for Work and Health, 2010, para. 10). Because of these determinates I think the probability can be inaccurate because the data is different per individual. For example, “according to insurance.com research, 16-year-old males pay on average $902 more than their female counterparts or 14% more” (Manning, 2019, para. 15). The probability based on gender falls under subjective probability because it is based on the information at hand.

References Institute for Work and Health. (2010, October). Probability. Institute for Work and Health. https://www.iwh.on.ca/what-researchers-mean-by/probability#:~:text=Probability% 20provides%20information%20about%20the,the%20risk%20of%20health%20effects . Lind, D., Marchal, W., & Wathen, S. (2019). Basic statistics for business & economics. McGraw-Hill Education. Manning, S. (2019, March 16). Are men better drivers than women? Insurance.com. https://www.insurance.com/auto-insurance/safety/are-men-better-drivers-than- women.aspx#:~:text=According%20to%20insurance.com%20research,least%20for %20a%20little%20while .

Your preview ends here

Eager to read complete document? Join bartleby learn and gain access to the full version

Access to all documents
Unlimited textbook solutions
24/7 expert homework help

Natasha, I liked your example of the weather forecast as an everyday probability. When planning an event outside and I need to know the weather for that specific day, I rely on the probability of rain provided by the forecasters. “Forecasts of the probability of precipitation occurrence have been made for several decades and are well accepted, even if not always properly interpreted” (AMS Council, 2008, para. 5). Other forecast elements, such as hurricanes and tornados, are tracked via temporal characteristics to quantify uncertainty (AMS Council, 2008). There are many advantages to using a probability forecast, such as the ability to weigh the cost of acting versus the cost of not acting (AMS Council, 2008). While there is uncertainty with our current weather system, it is still highly relied upon by society. References AMS Council. (2008, May 12). Enhancing weather information with probability forecasts. American Meteorological Society. https://www.ametsoc.org/index.cfm/ams/about-ams/ams-statements/statements-of-the- ams-in-force/enhancing-weather-information-with-probability-forecasts/#:~:text=A %20probability%20forecast%20includes% 20a,accurately%20quantifies%20the %20inherent%20uncertainty .

Emma, I liked your statement about the problems with real estate statistics. I found an article by Souerbry (2018) that lists some other misleading statistics in real estate, including listing price versus sale price, price per square foot, and days on market. The statistic that I would like to share with you is the days on the market because it can be confusing. “In general, the number of Days On Market (DOM) refers to how long a home is offered for sale before an offer is accepted” (Souerbry, 2018, para. 4). When a home is on the market for a long time, then it can be negatively viewed. Reasons for real estate to have a high DOM are factors such as the selling price being too high or issues with the home (Souerbry, 2018). To combat this negative statistic, realtors will take the estate off the market and then relist it to set the DOM back to zero. I hope you find this as interesting as I did! References Souerbry, J. (2018, December 26). Beware of misleading real estate statistics. Word Press. https://cordonrealestate.com/misleading-real-estate-statistics/

DB Week #3

Related Documents