Study On Utility Charges In A Large City Of United States

Analysis of Utility Charges Pattern in Large City

Living in a city demands planning of family budget, the choice of housing property asserts the type of utilities to be paid at the end of a month. Bigger cities provide better facilities, but at the same time utility charges for those facilities push a person into pocket crunch. Water bill, electricity bill, house rent, property tax, cooking gas charges increases the monthly expenditure of a person. Even living charges in a city differs from one place to another place, from one housing project to another housing project. To study the pattern of variation in cost due to utility charges, the current study selected a large city of United States.

Utility charges of 50 one bedroom apartments were collected for analysis of the trend of cost in the city. Various aspects were studied, the sample descriptive was analyzed, and normality of the utility data was checked. The study tried to relate the expenditure pattern and housing project pattern of the selected city. Daily needs and parallel functioning of monthly requirements takes a toll on a person. Along with the ever growing burdens of routine life, searching a proper location for house property is a cumbersome job. A person will always like to have a readymade data analysis in front to decide on the location of house project. The current work has been done to provide such kind of data analysis, where a person can look at the pattern of utility expenditure around the city. This study is supposed to help the city administration also, in investigating the civil requirements of the society.

Utility charges for the month of July 2010 were used to construct a box plot. The distributional pattern of the sample data was studied from the box plot. The entire data set was divided into four equal parts; the box plot reflects the four equal portions of the data. The first leg of the box plot reflects the first quartile, the median was observed to be near $150. The third quartile was near $175 and the first quartile was near $125. The maximum range of utility charges extended beyond $200. No outlier values were seen in the plot. The distribution was equally spread in the four parts of the plot. Therefore the normal nature of the utility data was evident. Hence, it was easy to conclude that utility charges across the city were evenly distributed for different housing projects. Therefore it was buying a property in the chosen city did not involved the utility charge factor.

Construction of Box Plot

The sample data was also plotted in a normal Q-Q plot. For the construction of normal Q-Q plot in MS Excel, probabilities for the 50 sample data were evaluated. The z- scores and standardized value of the utility charges were also found. The Normal Q-Q plot consisted of z-scores and standardized value of utility charges[1]. The data was then arranged in ascending order for construction of Q-Q plot. The trend of the Q-Q curves reflected an almost normal data. The quantiles basically reflect the percentiles, and the distribution of data on the both side of the vertical axis was observed. Half of the data was plotted in the left half of the graph and rest of the half was plotted in the right hand side of the vertical axis. This equal distribution of the data point reflected the normal nature of utility charges. The symmetric pattern of the distribution reflected that the choice of housing project was not dependent on the amount of utility bill[2].

Table 1: Normal Q-Q plot data in tabular form

Data from table 1 were used to construct the normal Q-Q plot.

Utility charges were then plotted in a histogram. The maximum utility charge of the city was calculated as $ 213 and the minimum utility charge was $ 82. The sample data was distributed in 14 classes with bin values as the mid points of the class intervals. Frequencies for the classes were calculated and the entire distribution has been provided in table 1. 

Table 2: Frequency distribution of utility charges

MS Excel chart builder was used to create the histogram. The distribution pattern of the data got revealed from the histogram. A negative exponential trend was visible from the graph. The frequencies of the data were accumulated near the median value. No outlier value was present in the data. The graph pattern re-established the normality of utility charges. The frequency distribution outline disclosed that utility charges of a substantial number of cities were clustered near $ 155 mark. The choice of building project in different part of the city was independent on the factor of the study[3]. 

The descriptive values were calculated using MS Excel 2007 version. The values of the descriptive have been provided in table 1. The average utility charge was $ 147.06 with standard deviation of $ 31.69. The median of the data was $ 148.50 and it was almost equal to the mean. The distribution curve was expected to follow the Gaussian curve because of the equality of mean and median. The minimum utility charge was found as $ 82 and maximum was $ 213. The value of the first quartile was $ 127.25 and the mode of the data ($ 130) was near first quartile. The third quartile was at $ 167.25. The Interquartile range was calculated as $ 40.5 and it was 1.28 times that of standard deviation (SD). The range of the data was $ 131and it was almost four times the SD, this reflected that the data was evenly spread in all quartiles. As the distribution curve was earlier found to be normal, the fact was verified with a cross check study with the skewness and kurtosis of the data. Both skewness and kurtosis were almost zero and confirmed the normal nature of the frequency distribution[4]. The spread of the data was calculated as , the interval was calculated as [115.4, 178.8] and 34 data point were within the range which was 68% of the total data[5]. The spread was increased to , the interval was calculated as [106.5, 187.6] and 40 observations were within the interval. This was 80% of the data points. The accumulation trend and nature of the confidence interval pattern reflected the results of earlier analysis. The interval of was [85.0, 209.2], which enclosed all the data points. The tails of the normal curve were less populated with utility data and consequently very less probability was distributed in the tails. The accumulation towards the median of the data reflected that the city housing projects had almost equal amount of average utility charge in a month[6]. 

Normal Q-Q Plot

Table 3: Descriptive value of utility charges

Conclusion

The sample data of the study was normally distributed and the results were highly acclaimed by the aspiring new home buyer. The facilities had reached to all corners of the city and charges were almost at par compared to different parts of the city[7]. The parity in facilities was reflection of the fact that development work has reached every corner of the city. Utility charges include house rent, electricity bill, water bill, cooking gas expenditure, and many more charges for essential daily needs[8]. The average cost equality reflected parity in rent of houses in different part of the city. Probably the communication system of the city was well diversified in nature, due to which equality in rent rate was possible. Other than house rent and city local taxes, electricity and water charges are generally equal in a large city. Hence, neglecting the peripheral factors, the house rent or one bedroom house price pattern reflected the overall development of the city of the study.  The cost theory, on international basis fails to analyse and present the exact nature of the social and financial condition of a country, for that matter a province or a large city. The study was conducted to plug in the loopholes of such theories. The results reflected that the model chosen in the study explained the true nature of the city[9].

There were certain limitations of the study as well. The utility cost did not have any sub- categories of costs; hence it was not possible to know the individual cost trend in the city. The effect size of the sample was not cross checked. Though the data was normally distributed, effect size of the sample was essential for any cross tabulation and inferential study. Linear regression model was ideal to cross check utility charges against satisfaction level or location of housing projects. Future scope of the study was to include these parameters in the research work. A comprehensive and extensive study could clarify all the questions related to the choice geographic location in a city. The extension of the work including different cities could reflect the variation of living cost in a province or in a country. Generalization of the model with more independent factors was essential to gain a substantial knowledge about the living cost for a country or a state[10].

References 

Aismoradi, Mojtaba, Hannele Turunen, and Terese Bondas. “Content analysis and thematic analysis: Implications for conducting a qualitative descriptive study.” Nursing & health sciences 15, no. 3 (2013): 398-405.

Bakija, Jon, Adam Cole, and Bradley T. Heim. “Jobs and income growth of top earners and the causes of changing income inequality: Evidence from US tax return data.” Unpublished manuscript, Williams College(2012).

Chambers, John M. Graphical Methods for Data Analysis: 0. Chapman and Hall/CRC, 2017.

Doane, David P., and Lori E. Seward. “Measuring skewness: a forgotten statistic?.” Journal of Statistics Education 19, no. 2 (2011).

Ghasemi, Asghar, and Saleh Zahediasl. “Normality tests for statistical analysis: a guide for non-statisticians.” International journal of endocrinology and metabolism 10, no. 2 (2012): 486.

Ghasemi, Asghar, and Saleh Zahediasl. “Normality tests for statistical analysis: a guide for non-statisticians.” International journal of endocrinology and metabolism 10, no. 2 (2012): 486.

Handbury, Jessie. “Are poor cities cheap for everyone? non-homotheticity and the cost of living across us cities.” (2013).

Huang, Haifang, and Yao Tang. “Residential land use regulation and the US housing price cycle between 2000 and 2009.” Journal of Urban Economics 71, no. 1 (2012): 93-99.

Michalos, Alex C. Environment, Transportation, and Housing: A Comparative Study of the Quality of Life in Canada and the USA from 1964 to 1974. Vol. 4: Environment, Transportation and Housing. Vol. 4. Springer Science & Business Media, 2012.

Nikitina, Oksana, Elena Alpatova, Elena Shershneva, and Konstantin Rutkauskas. “Urban management: Addressing the housing and utility challenge.” In E3S Web of Conferences, vol. 6. EDP Sciences, 2016.

O’Connor, Nicole, and Rachelle Macur. “Engaging residents in affordable housing—resident engagement pilot at Denver Housing Authority Westridge apartments.” Energy Efficiency (2018): 1-16.

Saiz, Albert. “The geographic determinants of housing supply.” The Quarterly Journal of Economics 125, no. 3 (2010): 1253-1296.

Triola, Mario F. Elementary statistics using Excel. Pearson, 2013. 

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