Is it reasonable to choose a subset of the time series to create a model? I am new to time series forecasting and would much appreciate your reply. I have an annual housing starts data between1970-2022 shown, below.

I want to forecast the next 10 year housing units. If I use the whole series to model, the prediction interval is quite wide, below.

Based on a very similar project is here (https://towardsdatascience.com/applied-time-series-forecasting-residential-housing-in-the-us-f8ab68e63f94) I tried the last 12 year as predictor  and I get better accuracy, below.

But, is  choosing only last 12 year for modelling reasonable? I am concern that my model doesn’t capture the evolution of the data if I don’t use the whole series. Any suggestions? Thank you in advance.

change points
If you think there was a change point in 2010, then you'll get a better forecast by not using earlier data. The idea is that something has changed in 2010, and earlier data is irrelevant. So, by including an irrelevant information you make things worse.
The problem with this approach is that there can be another change point in future, and the model has no means to predict it. Prophet used to work like that when I looked at it a couple of years ago.
A variation of the idea is the regime change type of thinking, that your system switches between regimes, and you forecast accordingly. In this case you can predict probabilities of regimes, so it's a bit better than just assuming that the current regime will hold forever
exponential smoothing
A less arbitrary approach would be to weight the observations when estimating the coefficients of the model, e.g. the exponential weights so that earlier observations have lesser weight. This way you don't throw the old data out but its weight is lower than most recent data.
changes vs levels, unit root vs AR(1)
Is your data stationary? That's the question here to ask. Because, one way to interpret the historical data is that after the financial crisis, the housing starts dropped immediately but since then they were recovering to their balanced level of around 1,000. If this is the case then your first forecast seems more sensible, because you expect that the growth will slow down soon and there's a lot uncertainty as demonstrated with charts.
Your second forecast based on post-crisis data is what Greenspan called "irrational exuberance", that somehow housing market will be booming to infinity with much less uncertainty too. That's not a reasonable outlook
