I am using panel data for a difference-in-difference model in which I attempt to see the affect the London Olympics had on house prices in London. To do this I am comparing house price differentials between host boroughs of London (boroughs that hosted an Olympic event) and non-host boroughs across the time period of 2009-2015 (with the Olympics taking place in 2012) by using a dummy variables (Host) indicating which boroughs are hosts and (Games) indicating whether the time period is before or after 2012. I have annual data for housing transactions for each borough and therefore thousands of observations for each borough in each year and many transactions for the same price in the same borough and same year creating duplicates. My first issue is when I try and tell stata that I am using panel data with xtset it says "repeated time values within panel". I have read that this is due the duplicates and use of annual data and was wondering whether it is possible to still use this data and run regressions without using xtset.

My second issue is that I attempted running regressions without xtset and initially the coefficients were all very significant. However when I cluster the standard errors at the borough level, the p-values become very large and the coefficients become largely insignificant. I was wondering why this is and whether it is essential to cluster the standard errors at the borough level?

Code:
* Example generated by -dataex-. To install: ssc install dataex
clear
input long price float borough_id byte host
225500 1 1
226000 1 1
343000 1 1
365000 1 1
375000 1 1
390000 1 1
570000 1 1
151000 2 0
151500 2 0
152000 2 0
152000 2 0
152000 2 0
152000 2 0
152000 2 0
152000 2 0
152000 2 0
152000 2 0
152500 2 0
152500 2 0
153000 2 0
153000 2 0
153000 2 0
153000 2 0
153000 2 0
153000 2 0
153000 2 0
153000 2 0
153333 2 0
153599 2 0
154000 2 0
154000 2 0
154000 2 0
154000 2 0
155000 2 0
155000 2 0
155000 2 0
155000 2 0
155000 2 0
155000 2 0
155000 2 0
155000 2 0
155000 2 0
155000 2 0
155000 2 0
155000 2 0
155000 2 0
155000 2 0
155000 2 0
155000 2 0
155500 2 0
156000 2 0
156000 2 0
156000 2 0
156000 2 0
156000 2 0
156000 2 0
156000 2 0
157000 2 0
157000 2 0
157500 2 0
157500 2 0
157500 2 0
157500 2 0
158000 2 0
158000 2 0
159000 2 0
159000 2 0
159000 2 0
159000 2 0
159000 2 0
159000 2 0
159500 2 0
159800 2 0
160000 2 0
160000 2 0
160000 2 0
160000 2 0
160000 2 0
160000 2 0
160000 2 0
160000 2 0
160000 2 0
160000 2 0
160000 2 0
160000 2 0
160000 2 0
160000 2 0
160000 2 0
160000 2 0
160000 2 0
160000 2 0
160000 2 0
160000 2 0
160000 2 0
160000 2 0
160000 2 0
160000 2 0
160000 2 0
160000 2 0
160000 2 0
end
Many thanks