Getting "Outsiders"

Since our projects are relevant to today's society, would it not make sense for everyday people to see them? People other than our class peers, like parents, friends, or even people in a relative field to our projects, can see our posts and comment on our progress. Having an opinion outside of my statistics class can give me a better angle to present my data and make my project more appealing. I plan on getting my parents and possibly a real estate agent to see my blog and comment on my posts, especially the real estate agent. Having their opinion will give me an inside edge on the real estate field and to make sure what I present will be significant to our audience.

Data Collection

Sorry, I forgot to publish this post, so many of you just saw "I collected data."

Since I was out at LSU last week, I was not able to make any posts. I did collect some data over the weekend, however. I contacted Latter & Blum last Tuesday and asked for any listings that were sold in the past year or so in the New Orleans area. I told them about my project and what I intend to do with the data, and they were happy to give me the data. The hard part is that they gave me over 1,000 listings! I admit, 1,000 survey results would be a lot easier to record than 1,000 values, but alas, this is all for the benefit of my project. This week, I've been putting in the data in Excel, and trust me, it has not been fun. However, since we did record hundreds of values before in my statistics class, I feel that I am able to do so without any major problems or obstacles. As expected, some prices ranged from about $40,000 to $2,000,000, and properties ranged from one-bedroom small houses to even a four (yes, four) story house. It seems that even though we are still "rebounding" from hurricanes and the like, our real estate business is still going strong, and will continue to do so for the next year or so. The New Orleans selling price average (so far) seems to be a little ahead of both the Louisiana and national averages, but only by about ten thousand dollars. I can see if this difference is really a significant difference, or that it is really a small difference since the values are so high. This website: http://www.surveysystem.com/signif.htm gives a good description of "significance" in data, and even shows an example to find significance. I feel that I truly understand significance in stats, which is very beneficial for my project results.

Here's a link to help make graphs in Excel that I think will be very helpful: http://peltiertech.com/Excel/ChartsHowTo/

Changes to my Toolbox (Part 2)

So I changed my toolbox the first time in order to make it look professional. Turns out, I tried a little too hard doing so. Some of the explanations were a little hard to understand if the reader didn't know much about statistics. What I did was tone down the terms I used and made them easier to understand. Instead of finding prices of homes currently on sale, I have decided to find prices of houses bought since it will be much easier to find. I narrowed down my search to where I will collect my data from, and that is from Latter & Blum Inc/Realtors®. I will call their office in Metairie to find the prices of homes bought in the New Orleans area recently. Also, I changed the "who" of my project from the houses in New Orleans to the homebuyers themselves that have bought homes in the New Orleans area. If I'm trying to make my project relative to today's world, it must relate to the people.

Connecting to the Media

Now that I am starting to collect my data with my finished data analysis toolbox, what will I do with this data? If no one knows about it, what is it good for? Absolutely nothing! The data must be displayed in order to have any relevance, so I must find a media outlet in order to get my word across about New Orleans real estate trends. Using data from Latter and Blum Inc/Realtors®, I will have a legitimate report for the media to see. Since the topic of the real estate business has been really hot these past few months due to recent foreclosures and the like, my project is very relevant to the current issues in real estate, which will make it that much easier to find an outlet to use. I could use a television news station to reach a very broad audience, or I could just get an article for the Real Estate section of a local newspaper (Times-Picayune).

If there's one thing that I need to make the project a professional finding, I must make it appeal to my audience. Just saying what the data is just won't cut it. I must explain what this data means, what the trends are in the prices (if any), are the trends (or lack thereof) good or bad, and if the New Orleans area real estate business is getting better or worse. I've heard people say that Louisiana tends to do the opposite of what the rest of the nation is doing in terms of real estate, but what facts are there to (dis)prove it? The data will be used to inform the audience about New Orleans real estate so that potential homebuyers will know what to look forward to from recent sales history. This site (http://www.trulia.com/real_estate/New_Orleans-Louisiana/) shows trends in recent years in similar ways that I intend to present the trends that I find. It even shows how and why some trends may be occurring.

This link gives graphs of current trends in the New Orleans area: http://www.trulia.com/real_estate/New_Orleans-Louisiana/market-trends/

How I intend on collecting data

With my data analysis toolbox pretty much wrapped up, I can now start collecting the data. The question is: How am I going to collect the data? First of all, I will contact a few real estate agencies and ask for a number of house prices in the New Orleans area. I am not sure how many houses I will use per sample, though. I will also use web resources to find prices of houses and the average prices of Louisiana and the United States. I will determine whether or not I will find prices of identical homes, probably by square footage and/or amount of floors. Anyone have any ideas or suggestions?

Changes to the Toolbox

For our NOLA project, we had to create a data analysis toolbox of our project. It explains the who, what, when, where, how, and why of our project/experiment, and what graphical displays we will use to present our data. My toolbox, much like my other classmates' toolboxes, had some errors and was not up to standards of what a toolbox should look like. For example, I did not go into detail about the specifics of my project, like the individuals in the data and the date and location of which the project would take place. I also did not explain what the numerical summaries and the graphical displays were, so if someone were to read my toolbox, and didn't know anything about a box plot, then they would have no idea why I would have one. I also did not include the question that I plan to answer with the outcome of this project, so the reader would have no idea why I am looking up house prices in New Orleans and comparing them.

To change my toolbox, I modeled mine after a toolbox my teacher made for a previous project. I made sure that I would answer the who, what, when, where, how, and why; and explained each and every numerical summary and graphical display that I would use so that the reader is informed of everything I intend to do and what I am using. I also added the question of which I intend to answer from this project so the reader knows exactly what I am looking for; that I am not just randomly finding the housing prices of New Orleans area homes. I started with just using the five number summary, a histogram, line graph, and box plot. I did not explain what they were and how they are used, but I explained so in my revision. I even added a few more things I will use, including a two-sample t test, the mean, the standard deviation, and any outliers that may come into effect. To explain the two-sample t test, I used this link (http://europe.isixsigma.com/library/content/c070613a.asp) that explained how to do this test and how to make sense of it.


Here's a link from Latter and Blum President Arthur Sterbcow discussing the housing prices in New Orleans in December 2007: http://www.youtube.com/watch?v=r0_zHfJTszs

What makes a good blog?

A blog entry must not have just fancy words and nice pictures or graphics to go along with it. It must have substantial information that is relevant to the topic and the picture or visual aid must also be relevant to the topic. When you look at the pictures or visual aids, you should be able to infer what the entry is about. There must be facts and/or statistics that help explain the topic of the post, instead of just random opinions, so that it does not seem that the entry was made randomly. This especially separates the great blogs from the average blogs. Instead of trying to look flashy, it is important that the content is the focal point of your blog. This has been stated by my teacher for my project, so I try to make the content the reason for people to see my blog. For example, this blog entry from Nola.com's real estate blog talks about the growing communities of St. Tammany Parish, particularly in Covington and Mandeville. The entry uses many facts to support its topic, including the number of houses under contract from a realtor company and the number of houses on the market in recent months. The points made are not only supported by these facts, but are the foundation of other points established in the entry. There are a few pictures of scenery that give a feel of some of the properties available in some of the St. Tammany Parish properties. Documentation is also very helpful to the validity of your entry. In this entry, the author uses quotes from interviewing people in the real estate business from various companies.

So, to sum up a good blog entry, you must have good content, the visual aids must be relevant to the topic, and you cannot stray from your topic.

Here's a link to my example blog: http://blog.nola.com/realestate/2009/03/opendoor_policy_covington_mand.html