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by: Miss Adeline Weimann


Miss Adeline Weimann
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This 7 page Class Notes was uploaded by Miss Adeline Weimann on Wednesday September 9, 2015. The Class Notes belongs to ECON 482 at University of Washington taught by Staff in Fall. Since its upload, it has received 21 views. For similar materials see /class/192488/econ-482-university-of-washington in Economcs at University of Washington.




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Date Created: 09/09/15
How to Find a Reasonable Rental in Seattle Christina Wissink 0121700 Econ 482 cwissinkuwashingtonedu Your 482 Paper has been received Summary Seattle is one of the top ten most expensive cities in the US to live this creates a problem for one near graduation University of Washington senior You see while I am enrolled in school my parents who happen to live 1700 miles away in much less costly Oshkosh WI have agreed to pay for all of my tuition and living expenses however the day I graduate I am on my own With this in mind I do not see myself able to continue living solo in my studio apartment for 575 a month So I set out to nd a cheaper living arrangement in the Emerald City I first divided Seattle into 23 different districts ranging in distance from Lake City to West Seattle Then I paged through the Stranger and searched the NW Classifieds Seattle Rentals and Apartmentscom for two bedroom apartments throughout the Seattle area I collected 314 data points roughly 1314 rental rates per district I also gathered data on factors Ithought would affect the rental price of an apartment These factors include distance from downtown average age of the neighborhood residents average income data found at seattlepinwsourcecomwebtowns percentage of rental units and percentage of white residents After running a least squares regression on the data it was determined that one can find a reasonable rental in Seattle by incorporating these factors The factors that had the largest affect on the data are distance from downtown 5220mile age 2164year and the percentage of rental units in the neighborhood 687percent Determining the Factors Since I was determined to find the cheapest possible two bedroom apartment I needed to predict some factors that would in uence the rental price The first and most obvious one I thought of was distance from downtown Downtown Seattle is the place to see and be seen major department stores and novelty shops are located downtown and along the waterfront many bars and clubs are found Downtown Belltown and Pioneer Square large concert venues and sports arenas can be found here and not to mention a popular Seattle tourist attraction The Pike Place Market So it seems obvious that the further away one gets from the hustle and bustle of downtown the less they will pay for a place to live I marked downtown Seattle at 63911 Avenue and Cherry Street and used Yahoo Maps to determine the distance of other districts from downtown the furthest neighborhood being Lake City at 99 miles And after running the least squares regression it is in fact the case that for each mile away from downtown one will save around 5220 on their monthly rent I also thought the income of the renter would have a large in uence over rental price but this turned out not to be true I predicted the neighborhood with the highest average income to be either Downtown or Belltown when in actuality the average income for those districts is 24678 and 35140 respectively The area with the highest average income is the Madison ParldMadrona area earning an average of 75034 per year and average monthly rent being 1005 The least squares regression confirmed that the renter s income has little to no effect on their monthly rent One surprising factor turned out to be average age of the renter When I gathered data on average age of residents there didn t seem to be much variance between neighborhoods Further more surprising was the youngest University District at 24 years of age and the oldest Madison ParldMadrona at 45 years have fairly close rent prices at 1048month and 1005month respectively their distances from downtown varied by only 13 miles the UDistrict is 46 miles away while Madison ParldMadrona is 33 For each year of age the renter can add 2164 to their monthly rent This can logically be explained On average a person in their 40s is well into hisher career making a sufficient amount of money and able to afford a nicer place to live or own their own house whereas a 24 year old recent college grad is just starting their occupation may change it a few more times before they find a job that they enjoy and are hardly making enough money to support themselves However it is difficult to control for age because it is inevitable that each year that passes we become a year older even though many people especially women seem to remain 29 years old for as long as we can remember The last major variable in determining rent price is the percentage of rental units in the area Neighborhoods with a high percentage of renters include Downtown 84 UDistrict 82 Capitol Hill 76 and Wallingford 76 According to the least squares regression for each renter percentage point one should add 687 to the price of rent The positive relation between percentage and price can be understood through basic economic supply and demand The supply curve for rental units is nearly vertical limited supply due to the fact that rental units cannot be created in only a few days so when more households demand units for rent the demand curve shifts to the right causing the price to increase How to reduce your rent According to the model there are several ways to reduce the price you pay for a space to live First get out of downtown I know there are people out there who have an extreme fear of leaving the comfort of downtown Seattle Yes it is true downtown seems to have everything so why would you want to leave The answer is simple to save you money Moving just 3 miles away to somewhere like Queen Anne or Eastlake will save you 15660 a month that s 187920 a year The extra 1879 comes in pretty handy for a starving 20 something 1eaming to budget hisher expenses for the rst time Also you might try to nd a neighborhood with a small percentage of renters Magnolia Greenlake and Greenwood are neighborhoods that have less than 50 of units for rent These areas are also more than 3 miles out of downtown saving you between 331990 and 476061 each year Testing the Results This model works well when trying to nd a reasonable rental in Seattle Itested the data in several different cases First I found the price for a two bedroom apartment in my current complex I did not include this price 915month in my observations Using the regression I calculated a rent price of 106071 This seems a little high but I am not able to control the age variable I am 20 years old or income currently I am a starving student with 0 income Second last year I lived in Lake City with a roommate and paid 620 this observation was also not included in the observations The price calculated from the regression is 592month The error from the rst test is 159 and the error from the second test is 452 Conclusion Although there is a small error term it is possible to nd a reasonably priced unit in Seattle Adding more variables may make this model more accurate but the key is to determine which ones Initially I included two dummy variables whether or not there is a hospital in the neighborhood and whether or not there is a university in the neighborhood These dummy variables seemed to skew the data I ran practice tests with the initial regression data and received an accurate price for a unit in the University District a neighborhood with both a hospital and university but a very inaccurate price for a unit in Lake City a neighborhood without either a hospital or university After omitting these two variables the regression provides a means to nd a reasonable rental in Seattle The most important factors to keep in mind are choosing a home outside of downtown and nding a neighborhood with a small percentage of renters Dependent Variable PRICE Method Least Squares Date 120803 Time 1208 Sample 1 23 Included observations 23 Variable Coefficient Std Error tStatistic Prob C 1763001 6270116 0281175 07820 AGE 2163682 1187893 1821445 00862 DIST 5219964 2052976 2542633 00210 INCOME 0004708 0004989 0943535 03586 PERRENT 6871180 4961659 1384856 01840 PER WHITE 1734590 3081109 0562976 05808 Rsquared 0676091 Mean dependent var 1034377 Adjusted Rsquared 0580824 SD dependent var 2768154 SE of regression 1792208 Akaike info criterion 1343457 Sum squared resid 5460415 Schwarz criterion 1373079 Log likelihood 1484976 Fstatistic 7096788 DurbinWatson stat 2144179 ProbFstatistic 0000945


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