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sdrealtor
ParticipantI agree with contrarian also but its not just uber wealthy folks. The average Euro has seen their currency appreciate by 50% vis a vis the US $ the last few years. That means we are effectively at roughly 2001 pricing for them.
ParticipantI agree with contrarian also but its not just uber wealthy folks. The average Euro has seen their currency appreciate by 50% vis a vis the US $ the last few years. That means we are effectively at roughly 2001 pricing for them.
ParticipantI agree with contrarian also but its not just uber wealthy folks. The average Euro has seen their currency appreciate by 50% vis a vis the US $ the last few years. That means we are effectively at roughly 2001 pricing for them.
ParticipantI agree with contrarian also but its not just uber wealthy folks. The average Euro has seen their currency appreciate by 50% vis a vis the US $ the last few years. That means we are effectively at roughly 2001 pricing for them.
ParticipantI agree with contrarian also but its not just uber wealthy folks. The average Euro has seen their currency appreciate by 50% vis a vis the US $ the last few years. That means we are effectively at roughly 2001 pricing for them.
ParticipantThe difference is you are using a model and I am basing my stats on real street level market information. My house was never worth $1m and the 950K would have required a lucky sale at the absolute peak. My neighborhood is very representative of the overall market and if anything has been stronger in the decline.
The problem with using stats looking down from cyberspace is that you use examples like Circuola Sequoia and Swami’s Lane which were new purchases. Both required landscaping, window treatments and assorted other improvements to make them liveable which could (and did) easily add 10% to the purchase price you used. The Swami’s house was in a new tract close to the beach which had over 1000 people trying to buy about 30 homes. More than half of them went to friends and family of the builder. If they were sold on the open market the prices would have been much higher. I was on the list to buy one myself. These are examples of the kind of noise present in the data.
I think what you tried to do is great. It was a valiant effort but you are trying to do something which quite simply cant be done with any degree of accuracy. Sure trends emerge (prices increased from 2000 to 2005/6 and now they are falling….DUH!) but they can be observed equally with common sense. What has happened and what really is happening cant be accurately determined from cyberspace.
ParticipantThe difference is you are using a model and I am basing my stats on real street level market information. My house was never worth $1m and the 950K would have required a lucky sale at the absolute peak. My neighborhood is very representative of the overall market and if anything has been stronger in the decline.
The problem with using stats looking down from cyberspace is that you use examples like Circuola Sequoia and Swami’s Lane which were new purchases. Both required landscaping, window treatments and assorted other improvements to make them liveable which could (and did) easily add 10% to the purchase price you used. The Swami’s house was in a new tract close to the beach which had over 1000 people trying to buy about 30 homes. More than half of them went to friends and family of the builder. If they were sold on the open market the prices would have been much higher. I was on the list to buy one myself. These are examples of the kind of noise present in the data.
I think what you tried to do is great. It was a valiant effort but you are trying to do something which quite simply cant be done with any degree of accuracy. Sure trends emerge (prices increased from 2000 to 2005/6 and now they are falling….DUH!) but they can be observed equally with common sense. What has happened and what really is happening cant be accurately determined from cyberspace.
ParticipantThe difference is you are using a model and I am basing my stats on real street level market information. My house was never worth $1m and the 950K would have required a lucky sale at the absolute peak. My neighborhood is very representative of the overall market and if anything has been stronger in the decline.
The problem with using stats looking down from cyberspace is that you use examples like Circuola Sequoia and Swami’s Lane which were new purchases. Both required landscaping, window treatments and assorted other improvements to make them liveable which could (and did) easily add 10% to the purchase price you used. The Swami’s house was in a new tract close to the beach which had over 1000 people trying to buy about 30 homes. More than half of them went to friends and family of the builder. If they were sold on the open market the prices would have been much higher. I was on the list to buy one myself. These are examples of the kind of noise present in the data.
I think what you tried to do is great. It was a valiant effort but you are trying to do something which quite simply cant be done with any degree of accuracy. Sure trends emerge (prices increased from 2000 to 2005/6 and now they are falling….DUH!) but they can be observed equally with common sense. What has happened and what really is happening cant be accurately determined from cyberspace.
ParticipantThe difference is you are using a model and I am basing my stats on real street level market information. My house was never worth $1m and the 950K would have required a lucky sale at the absolute peak. My neighborhood is very representative of the overall market and if anything has been stronger in the decline.
The problem with using stats looking down from cyberspace is that you use examples like Circuola Sequoia and Swami’s Lane which were new purchases. Both required landscaping, window treatments and assorted other improvements to make them liveable which could (and did) easily add 10% to the purchase price you used. The Swami’s house was in a new tract close to the beach which had over 1000 people trying to buy about 30 homes. More than half of them went to friends and family of the builder. If they were sold on the open market the prices would have been much higher. I was on the list to buy one myself. These are examples of the kind of noise present in the data.
I think what you tried to do is great. It was a valiant effort but you are trying to do something which quite simply cant be done with any degree of accuracy. Sure trends emerge (prices increased from 2000 to 2005/6 and now they are falling….DUH!) but they can be observed equally with common sense. What has happened and what really is happening cant be accurately determined from cyberspace.
ParticipantThe difference is you are using a model and I am basing my stats on real street level market information. My house was never worth $1m and the 950K would have required a lucky sale at the absolute peak. My neighborhood is very representative of the overall market and if anything has been stronger in the decline.
The problem with using stats looking down from cyberspace is that you use examples like Circuola Sequoia and Swami’s Lane which were new purchases. Both required landscaping, window treatments and assorted other improvements to make them liveable which could (and did) easily add 10% to the purchase price you used. The Swami’s house was in a new tract close to the beach which had over 1000 people trying to buy about 30 homes. More than half of them went to friends and family of the builder. If they were sold on the open market the prices would have been much higher. I was on the list to buy one myself. These are examples of the kind of noise present in the data.
I think what you tried to do is great. It was a valiant effort but you are trying to do something which quite simply cant be done with any degree of accuracy. Sure trends emerge (prices increased from 2000 to 2005/6 and now they are falling….DUH!) but they can be observed equally with common sense. What has happened and what really is happening cant be accurately determined from cyberspace.
ParticipantBear in mind that my house did not sell in either time period. If it did, it could have sold above or below the values i used based upon the skill of the agent in pricing the property, the sellers motivation and many other factos. However, the numbers I presented are very solid typical cases of what price levels were and are in my submarket. As it is, esmith’s data as well as the case shiller figures dramatically overstate bubble appreciation and understate bust depreciation thus far IMO>
People get too excited over some whizbang statistical analysis. There is just too much noise in the data. I dont trust any data points. Whenever, i look at a comp I always question the price. Sometimes people sold too cheap and sometimes they got lucky with a high price. Sometimes the data is entered improperly or there are incentives undislcosed. Sometimes agents get paid and sometimes there are fsbo sales. Nearly every data point has quirks of some regard. I prefer to dwell in the world of theoretical price levels that existed at a point in time and what they are currently. I find this to be a much more reliable indicator of what is really happening.
ParticipantBear in mind that my house did not sell in either time period. If it did, it could have sold above or below the values i used based upon the skill of the agent in pricing the property, the sellers motivation and many other factos. However, the numbers I presented are very solid typical cases of what price levels were and are in my submarket. As it is, esmith’s data as well as the case shiller figures dramatically overstate bubble appreciation and understate bust depreciation thus far IMO>
People get too excited over some whizbang statistical analysis. There is just too much noise in the data. I dont trust any data points. Whenever, i look at a comp I always question the price. Sometimes people sold too cheap and sometimes they got lucky with a high price. Sometimes the data is entered improperly or there are incentives undislcosed. Sometimes agents get paid and sometimes there are fsbo sales. Nearly every data point has quirks of some regard. I prefer to dwell in the world of theoretical price levels that existed at a point in time and what they are currently. I find this to be a much more reliable indicator of what is really happening.
ParticipantBear in mind that my house did not sell in either time period. If it did, it could have sold above or below the values i used based upon the skill of the agent in pricing the property, the sellers motivation and many other factos. However, the numbers I presented are very solid typical cases of what price levels were and are in my submarket. As it is, esmith’s data as well as the case shiller figures dramatically overstate bubble appreciation and understate bust depreciation thus far IMO>
People get too excited over some whizbang statistical analysis. There is just too much noise in the data. I dont trust any data points. Whenever, i look at a comp I always question the price. Sometimes people sold too cheap and sometimes they got lucky with a high price. Sometimes the data is entered improperly or there are incentives undislcosed. Sometimes agents get paid and sometimes there are fsbo sales. Nearly every data point has quirks of some regard. I prefer to dwell in the world of theoretical price levels that existed at a point in time and what they are currently. I find this to be a much more reliable indicator of what is really happening.
ParticipantBear in mind that my house did not sell in either time period. If it did, it could have sold above or below the values i used based upon the skill of the agent in pricing the property, the sellers motivation and many other factos. However, the numbers I presented are very solid typical cases of what price levels were and are in my submarket. As it is, esmith’s data as well as the case shiller figures dramatically overstate bubble appreciation and understate bust depreciation thus far IMO>
People get too excited over some whizbang statistical analysis. There is just too much noise in the data. I dont trust any data points. Whenever, i look at a comp I always question the price. Sometimes people sold too cheap and sometimes they got lucky with a high price. Sometimes the data is entered improperly or there are incentives undislcosed. Sometimes agents get paid and sometimes there are fsbo sales. Nearly every data point has quirks of some regard. I prefer to dwell in the world of theoretical price levels that existed at a point in time and what they are currently. I find this to be a much more reliable indicator of what is really happening.
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