The council of Maple County, concerned about the county's becoming overdeveloped, is debating a proposed measure that would prevent the development of existing farmland in the county. But the council is also concerned that such a restriction, by limiting the supply of new housing, could lead to significant increases in the price of housing in the county. Proponents of the measure note that Chestnut County established a similar measure ten years ago, and its housing prices have increased only modestly since. However, opponents of the measure note that Pine County adopted restrictions on the development of new residential housing fifteen years ago, and its housing prices have since more than doubled. The council currently predicts that the proposed measure, if passed, will result in a significant increase in housing prices in Maple County.
Write a response in which you discuss what questions would need to be answered in order to decide whether the prediction and the argument on which it is based are reasonable. Be sure to explain how the answers to these questions would help to evaluate the prediction.
The prediction about the proposed measure to limit the development of new housing in Maple county will result in a significant increase in housing price of Maple county was made on the premise of similar steps taken 15 years ago by Pine county where prices were increased by more than double and was also made by negating the consequence of the steps taken 10 years ago in Chestnut county where prices have increased moderately. However, the prediction does not make a cogent case since it is rife with holes and unwarranted assumption. However, before the prediction can be properly evaluated, three following questions must be answered.
First of all, does Pine and Maple County comparable? In other words, are the characteristics of their geography such as its area, nearby locations, presence of industrial and economic hubs, residential population, availability of transport and its connectivity with other counties same? If not, the prediction may be flawed. For instance, it may found that prices of Pine county increased in last 15 years immediately after implementing the restriction on new supply of housing because of the presence of industrial hubs in and/or around this county which made the people to stay and push other people to move to this county in pursuit of economic opportunities. However, this may not be the case with Maple county. It may be the county where no development opportunities exist and where the possibility of establishing a business cluster is close to nil. Consequently the restriction on supply of housing may not put any upward pressure on pricing since residents may not likely to stay in Maple county. Moreover, the question must be answered to know, if the changes occurred in last 15 years in Pine county and existing conditions in Maple county are same. For instance, when the restrictions were imposed 15 years ago in Pine, the conditions such as shortage in housing or more demand for housing or more demand for land for future projects of housing were in existence which pushed the prices upward. However, the time that exists today may not be the same as it was for Pine 15 years ago. For instance, today the supply of housing is abundance and there is no possibility for a demand for housing to increase in future. Therefore, restrictions on new supply may not result in price increase. If the above examples are true, then the prediction does not hold water.
Secondly, do Chestnut county and Maple county comparable? If yes, what are their similarities and can those similarities have an impact on prices of housing. For instance, it may happen that Chestnut county has similar features as that of Maple such as no. of residents, estimates on no. of people likely to move to Maple county, income levels of the residents, taxation on housing and wealth and economic prospects. If, no. of residents are same in both the counties along with the same prediction for future demand of housing for both counties keeping other things constant in both counties, then price may increase moderately in Maple county. Moreover, the question must also address the changes happened in Chestnut in last 10 years with the comparison to that of in Maple county. For instance, the conditions which were appropriate to put the restriction on housing supply in Chestnut 10 years ago, are also existing in Maple county today such as no. of houses already occupied, new housing projects sanctioned, demand for new housing and willingness of people to stay in Chestnut and move from other county to Chestnut, then price may increase only my moderate amount. If any of the above examples has merit, then the prediction drawn in the main argument is significantly weakened.
Third, does supply of new housing responsible for price change? In other words, if the supply of new housing changes, does it possible that price of housing will change just because of its supply? It may happen that there are other factors which may be responsible for price change. For instance, the price rise in Pine county was because of rising income levels of residents. In other words, income of the residents of Pine county may have increased substantially and they might have interested in investments in physical assets such as housing for long term advantage, therefore, the restrictions on supply increased the prices of housing. Question must be also asked on general price levels or comparable price index across Pine and Maple county. It may happen that though the price in Pine county has increased significantly, but the absolute value of price is same as that of Maple county. For example, the initial price was USD 100,000 for 100 square meter house and it has increased to USD 220,000 today, but the price in Maple county for the same size of house is USD 150,000 and people are unwilling to stay in Maple county for some reason. Then, supply restrictions may not result in price increase in Maple county. Unless the prediction is fully representative, valid and reliable, it cannot be used to effectively support the argument.
In conclusion, the prediction as it stands now, is flawed due to its reliance on several unwarranted assumption. If the county is able to answer the above three questions and offer more evidence, perhaps in the form of research study covering the conditions in these counties and reasons for price increase apart from supply, then it will be possible to fully evaluate the viability of the proposed prediction.
Comments
Essay evaluation report
flaws:
No. of Words: 916 350
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Attribute Value Ideal
Final score: 5.0 out of 6
Category: Very Good Excellent
No. of Grammatical Errors: 0 2
No. of Spelling Errors: 0 2
No. of Sentences: 35 15
No. of Words: 916 350
No. of Characters: 4451 1500
No. of Different Words: 286 200
Fourth Root of Number of Words: 5.501 4.7
Average Word Length: 4.859 4.6
Word Length SD: 2.645 2.4
No. of Words greater than 5 chars: 320 100
No. of Words greater than 6 chars: 217 80
No. of Words greater than 7 chars: 165 40
No. of Words greater than 8 chars: 94 20
Use of Passive Voice (%): 0 0
Avg. Sentence Length: 26.171 21.0
Sentence Length SD: 16.676 7.5
Use of Discourse Markers (%): 0.971 0.12
Sentence-Text Coherence: 0.323 0.35
Sentence-Para Coherence: 0.445 0.50
Sentence-Sentence Coherence: 0.095 0.07
Number of Paragraphs: 5 5
Grammar and spelling errors:
Line 3, column 857, Rule ID: SENT_START_CONJUNCTIVE_LINKING_ADVERB_COMMA[1]
Message: Did you forget a comma after a conjunctive/linking adverb?
Suggestion: Consequently,
...ing a business cluster is close to nil. Consequently the restriction on supply of housing ma...
^^^^^^^^^^^^
Line 3, column 1616, Rule ID: IN_PAST[1]
Message: Did you mean: 'in the future'?
Suggestion: in the future
...ty for a demand for housing to increase in future. Therefore, restrictions on new supply ...
^^^^^^^^^
Line 7, column 964, Rule ID: CD_NN[1]
Message: Possible agreement error. The noun 'house' seems to be countable, so consider using: 'houses'.
Suggestion: houses
...ce was USD 100,000 for 100 square meter house and it has increased to USD 220,000 tod...
^^^^^
Transition Words or Phrases used:
also, but, consequently, first, however, if, may, moreover, second, secondly, so, then, therefore, third, apart from, as for, for example, for instance, in conclusion, such as, first of all, in other words
Attributes: Values AverageValues Percentages(Values/AverageValues)% => Comments
Performance on Part of Speech:
To be verbs : 38.0 19.6327345309 194% => OK
Auxiliary verbs: 27.0 12.9520958084 208% => Less auxiliary verb wanted.
Conjunction : 29.0 11.1786427146 259% => Less conjunction wanted
Relative clauses : 18.0 13.6137724551 132% => OK
Pronoun: 34.0 28.8173652695 118% => OK
Preposition: 134.0 55.5748502994 241% => Less preposition wanted.
Nominalization: 37.0 16.3942115768 226% => Less nominalizations (nouns with a suffix like: tion ment ence ance) wanted.
Performance on vocabulary words:
No of characters: 4561.0 2260.96107784 202% => Less number of characters wanted.
No of words: 915.0 441.139720559 207% => Less content wanted.
Chars per words: 4.98469945355 5.12650576532 97% => OK
Fourth root words length: 5.49990608324 4.56307096286 121% => OK
Word Length SD: 2.7290945824 2.78398813304 98% => OK
Unique words: 312.0 204.123752495 153% => OK
Unique words percentage: 0.340983606557 0.468620217663 73% => More unique words wanted or less content wanted.
syllable_count: 1443.6 705.55239521 205% => syllable counts are too long.
avg_syllables_per_word: 1.6 1.59920159681 100% => OK
A sentence (or a clause, phrase) starts by:
Pronoun: 7.0 4.96107784431 141% => OK
Article: 12.0 8.76447105788 137% => OK
Subordination: 10.0 2.70958083832 369% => Less adverbial clause wanted.
Conjunction: 2.0 1.67365269461 119% => OK
Preposition: 9.0 4.22255489022 213% => Less preposition wanted as sentence beginnings.
Performance on sentences:
How many sentences: 38.0 19.7664670659 192% => OK
Sentence length: 24.0 22.8473053892 105% => OK
Sentence length SD: 88.8167080372 57.8364921388 154% => OK
Chars per sentence: 120.026315789 119.503703932 100% => OK
Words per sentence: 24.0789473684 23.324526521 103% => OK
Discourse Markers: 5.39473684211 5.70786347227 95% => OK
Paragraphs: 5.0 5.15768463074 97% => OK
Language errors: 3.0 5.25449101796 57% => OK
Sentences with positive sentiment : 17.0 8.20758483034 207% => Less positive sentences wanted.
Sentences with negative sentiment : 8.0 6.88822355289 116% => OK
Sentences with neutral sentiment: 14.0 4.67664670659 299% => Less facts, knowledge or examples wanted.
What are sentences with positive/Negative/neutral sentiment?
Coherence and Cohesion:
Essay topic to essay body coherence: 0.12690859617 0.218282227539 58% => OK
Sentence topic coherence: 0.0442086285453 0.0743258471296 59% => OK
Sentence topic coherence SD: 0.0434220817295 0.0701772020484 62% => OK
Paragraph topic coherence: 0.089398357388 0.128457276422 70% => OK
Paragraph topic coherence SD: 0.0290288684142 0.0628817314937 46% => Paragraphs are similar to each other. Some content may get duplicated or it is not exactly right on the topic.
Essay readability:
automated_readability_index: 14.1 14.3799401198 98% => OK
flesch_reading_ease: 47.12 48.3550499002 97% => OK
smog_index: 8.8 7.1628742515 123% => OK
flesch_kincaid_grade: 12.7 12.197005988 104% => OK
coleman_liau_index: 11.9 12.5979740519 94% => OK
dale_chall_readability_score: 7.45 8.32208582834 90% => OK
difficult_words: 152.0 98.500998004 154% => OK
linsear_write_formula: 18.5 12.3882235529 149% => OK
gunning_fog: 11.6 11.1389221557 104% => OK
text_standard: 12.0 11.9071856287 101% => OK
What are above readability scores?
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Write the essay in 30 minutes.
Rates: 66.67 out of 100
Scores by essay e-grader: 4.0 Out of 6
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Note: the e-grader does NOT examine the meaning of words and ideas. VIP users will receive further evaluations by advanced module of e-grader and human graders.