[kidyankee764]

Originally Posted by ryanthegoldengod

You're really not listening to me at all. I didn't dispute the statistics. But never mind. We appear to be talking at each other.

I’d say “disagreeing on the significant of the stats” means you’re interpreting them differently. Regardless, again, you can’t change facts. If you want to discuss facts, then let’s do so.

[BobNJ1960]

Originally Posted by MrGompers

If I use your numbers that is still within one deviation from the mean and less than 1 percent. The raw number is 3.7 thousandths of one in your example. You multiply by 100 to get a percent because most people can better understand/visualize percentages vs decimals.

I used regression from the mean, it would probably be more accurate to use regression to the mean in this scenario. That basically states that as a data sets gets larger, outliers become less common i.e. "regress to the mean" A data set of 3.5 million is very large.

Regression from the mean is basically a fancy way of saying "margin of error" but again with a data set of 3.5 million your results will be very accurate.

So what do these fancy math terms mean in layman's terms ? It basically means that 13,000 people is statistically insignificant regarding this data set.

And you can go out 10 years too if you want, but you will still be within the margin of error and regression to the mean. You are also assuming that no one is filling those spaces vacated by the 13,000 who left. An analysis of income tax receipts during the last ten years or maybe even the next ten years should provide us the answer. Because if people are leaving with their incomes, income tax receipts will also fall.
And before you try to tell me - but taxes have risen - we can exclude those increases easily.

No one is filling those spaces. 13,000 is the net loss of residents via state-state migration.

Taxes rising is often the result of raising rates or making more things subject to tax.

If I sell 10 items at $9 this year, 9 at $10 next year, if a wise businessman I am fretting over losing 10% of my volume sold. Not simply saying "revenue did not drop".

[LMPA]

Perhaps this will be a bit more impactful to some, over 40K people have left since 2010/2011. This does not include data after July 2016. I would take a conservative guess and say from July 2016 to January 2018 could easily be another 10K - 15K, but probably closer to 15K - 20K.

State Migration Rates, Net Totals: 2011-2016

Connecticut had one of the highest percentages of net migration from July 2015 and July 2016 at 3.6 with only 5 states ahead of it.

I haven't added up the lost net revenue, but I'm sure those numbers are staggering as well.

https://www.irs.gov/statistics/soi-t...migration-data

[LMPA]

Hottest real estate markets, February posted:

https://www.realtor.com/research/hottestmarkets/

Dig into zip code and county information:

https://www.realtor.com/research/rep...ttest-markets/

[MrGompers]

Originally Posted by kidyankee764

Seeing that the PPSqFT lowered in most CT towns, that would tell me the price required to buy into those towns lowered, as well, Mr. Math.

Price per square foot is a terrible way to compare pricing since it doesn't compare similar houses. One can be a cape, one a ranch, one split level, etc. Styles affect pricing. Location also affects pricing. What is the price of a house that is next to train tracks vs one that's next to an open field ? Price per square foot doesn't account for that.

If you think this place is such a ****hole why do people keep coming here and asking "I have 800k can I afford Westport? if there was a shortage Westport wouldn't cost 800k.

[kidyankee764]

Originally Posted by MrGompers

Price per square foot is a terrible way to compare pricing since it doesn't compare similar houses. One can be a cape, one a ranch, one split level, etc. Styles affect pricing. Location also affects pricing. What is the price of a house that is next to train tracks vs one that's next to an open field ? Price per square foot doesn't account for that.

If you think this place is such a ****hole why do people keep coming here and asking "I have 800k can I afford Westport? if there was a shortage Westport wouldn't cost 800k.

Huh? Price per square foot is, by far, the most accurate way to compare side by side. It compares two nearly identical homes. Styles may affect what an individual person is willing to pay, but price per square foot of comps determines market value. Bad location = lower price per square foot. Good location = higher price per square foot. This is all real estate 101.

I’m not sure where anyone said CT is a “****hole”. It’s a nice state that I’d like to see thrive. Calling a spade a spade doesn’t mean anyone discussing the state’s issues is anti CT.

And re: Westport, you’re right - there’s not a shortage. Inventory is way up. That said, 800k is nothing in Westport so that price point moves - it’s the high end that’s not moving, as stated in other threads that you contributed to. If you look at Westport’s price per square foot, it’s steadily gone down while the average sale price went up - that means the massive homes 2-3x Westport’s average sale price are selling at quite a bit less than they used to. Median days on market in Westport is over 100 days - vs 23 US avg. Yikes.

[DigitalMilford]

Originally Posted by kidyankee764

Huh? Price per square foot is, by far, the most accurate way to compare side by side. It compares two nearly identical homes. Styles may affect what an individual person is willing to pay, but price per square foot of comps determines market value. Bad location = lower price per square foot. Good location = higher price per square foot. This is all real estate 101.

I’m not sure where anyone said CT is a “****hole”. It’s a nice state that I’d like to see thrive. Calling a spade a spade doesn’t mean anyone discussing the state’s issues is anti CT.

And re: Westport, you’re right - there’s not a shortage. Inventory is way up. That said, 800k is nothing in Westport so that price point moves - it’s the high end that’s not moving, as stated in other threads that you contributed to. If you look at Westport’s price per square foot, it’s steadily gone down while the average sale price went up - that means the massive homes 2-3x Westport’s average sale price are selling at quite a bit less than they used to. Median days on market in Westport is over 100 days - vs 23 US avg. Yikes.

Median on the market time is dragged upwards by houses that are 2.5 million plus that are slow to sell, generally, no matter how hot the economy. If you eliminate the 2.5 million+ market which doesn't exist most other places, days on market in Westport falls in to line.

[kidyankee764]

Originally Posted by DigitalMilford

Median on the market time is dragged upwards by houses that are 2.5 million plus that are slow to sell, generally, no matter how hot the economy. If you eliminate the 2.5 million+ market which doesn't exist most other places, days on market in Westport falls in to line.

Truth. An acquaintance in Westport listed their small house for 899, it sold in 3 weeks for ~ 905k.

[DigitalMilford]

Originally Posted by kidyankee764

Truth. An acquaintance in Westport listed their small house for 899, it sold in 3 weeks for ~ 905k.

The 800 - 1.3 million market in Westport is very hot. Luckily the days of knocking down an 800K house to build a 3 million dollar house are ending... for now.

[Scottamemnon]

Originally Posted by MrGompers

If I use your numbers that is still within one deviation from the mean and less than 1 percent. The raw number is 3.7 thousandths of one in your example. You multiply by 100 to get a percent because most people can better understand/visualize percentages vs decimals.

I used regression from the mean, it would probably be more accurate to use regression to the mean in this scenario. That basically states that as a data sets gets larger, outliers become less common i.e. "regress to the mean" A data set of 3.5 million is very large.

Regression from the mean is basically a fancy way of saying "margin of error" but again with a data set of 3.5 million your results will be very accurate.

So what do these fancy math terms mean in layman's terms ? It basically means that 13,000 people is statistically insignificant regarding this data set.

And you can go out 10 years too if you want, but you will still be within the margin of error and regression to the mean. You are also assuming that no one is filling those spaces vacated by the 13,000 who left. An analysis of income tax receipts during the last ten years or maybe even the next ten years should provide us the answer. Because if people are leaving with their incomes, income tax receipts will also fall.
And before you try to tell me - but taxes have risen - we can exclude those increases easily.

Come on stop with the holier than thou language here. Other people understand math. You are using intentionally confusing language in your descriptions to make CT look better. Percentages are common language understand by all. Even lowly Astrophysics graduates like myself.