Determining housing demands for frequent movers

The scenario of relocation in a short amount of time is a pretty common scenario for most families, business works, graduate students, ect. However, finding housing that is optimal for each end user seems to be a bit more difficult. Users on common housing apps like Zillow or Airbnb are always provided information regarding the surrounding areas and what sort of atmosphere an area is likely able to provide rather than another. The only problem with this sort of information, is that it needs to be tailored to the end user. Finding relevant data that would provide the user more demand for one location rather than another seems to be not so trivial of a task, given the needs and requirements of each user varies exponentially.

So how can we extract ample amounts of information regarding the common housing demands of users and what attracts then to certain locations? First off we can use current demographics charts from the US census to determine where the hot spot locations are in terms of populations. Given that centers for large conglomerates of people tend to generate the need for areas directed towards recreation, such areas are likely to create a demand for housing in the vicinity. Also, since demographics creates demands on the housing market, specifically examining the fluctuations in the housing market’s pricings is directly related to the demand for the housing in that area. Thus, by examining these fluctuations one can get an idea not only to the demand by users to a specific location, but also to relative value that piece of housing has based on location. But what is causing these Knowing this data, we are able to generate research for extracting the housing demands of frequent movers.

The origin of our research begins with obtaining population density maps and superimposing those onto normal maps, this in itself sifts the areas of sparse population from those of extreme density. At this point we need to get an idea of what a user who was in the scenario of having to relocate to one of these categories would want to be asked in order to sift from the available options. Such questioning would be attained by conducting at least five separate interviews from people who fit the demographics of our end users. Also these interviews need to specify varying levels of population density, so that all are accounted for. At this point we would have an idea of what characteristics of the area are integral in determining the optimal housing location for users. The next category of the problem to tackle is in terms of specifications of housing available for the user by selecting this property, and which take precedence. Data that targets this can be found by analysing the advertising patterns in utilities offered by housing companies, which is offered in housing articles that cover the utilities of housing companies with the most return on investment. At this point we have a solid foothold in knowing a large portion of data that creates demand for users to choose certain housing.

While such data does exist that could aid in determining the housing demands of users, it is understood that bias does exist in the fact that while the sampled users and I may be able to provide ample relevant data, this will not be able to cover the whole population’s demands. Given that our sample set of interviewers and data is not the population set such skews will always exist. However, to better account for this skew we would individualize each set of data so that minimal bias exists between interviews, but more specific to the end user, when it comes to being prompted on housing demand options, there will be the ability for them to have a much wider field of data to choose from. As for bias that exists in our preconceived ideas of correlating different sets of data together, like the population density with elevated surrounding house costs, these would be refuted or supported by each conducted interview to stray away bias. Thus, providing our group with a safe and sound means of how to extract data on the housing demands of users.