First use a binary 0 (no renovation) and 1 (renovation) which works perfect with logistic regression.
Using the exact date is a bad practice. It guides the model in the direction of over-fitting on specific dates. For example, a pattern from 2006 would be specific to that year and would not help the future years. As an alternative, binning on larger spans like 5 years, 10 years (depends on the context) seems as an improvement. For example:
bins = [1990, 2000], [2000, 2010], [2010, 2020]
[1990, 2000] $\rightarrow$ (1, 0, 0)
[2000, 2010] $\rightarrow$ (0, 1, 0)
[2010, 2020] $\rightarrow$ (0, 0, 1)
This approach also has a tendency to over-fit but over a larger time span. Also note that, this way, your model always has an expiration date, since if we pass the last bin in 2021, there is no bin to cover the year. And if we include [2020, 2030] now, there is no data to learn about this bin. And using [2020, forever] is equally useless for future.
I suggest using the age of construction and renovation which are generalizable. A 5 years old house in 2000 could help us infer about a 5 years old house in 2010, 2020, or 2030. For houses with no renovation, age could be set to -1, which works fine with logistic regression (experiment with 0 too). So as a final example:
renovation (has renovation, renovation age)
-1 (0, -1)
2010 in 2019 (1, 9)
Note that repetitive time features are OK. For example, "Spring", "Monday", or "8:00PM", etc.