Indeed, and not just building more in more at-risk places but also the cost of building materials, construction labor and code compliance requirements have all generally increased more than baseline inflation. Factors like these tend to greatly increase recent estimates vs historical.

I read a paper a few years back which dove into how the data sources for weather damage assessment have changed a lot over the years. Much of the increase is due to more complete reporting and changes in categorization. Also, nowadays more things are insured and modern IT has made gathering the insurance reporting far more exhaustive. Plus local, state and federal agencies responsible for relief and/or recovery are gathering and reporting increasing amounts of data with each decade since the 70s (in part because their budgets rely on it). Factors like these mean in prior decades the total damage costs may have been more similar to today's than they appear but a lot of the damage data we gather and report now wasn't counted or gathered then.

Although I have no experience related to weather science, I remember the paper because it made me realize how many broad-based, multi-decadal historical data comparisons we see should have sizable error bars (which never make it into the headline and rarely even into the article). Data sources, gathering and reporting methods and motivations are rarely constant on long time scales - especially since the era of modern computing. Of course, good data scientists try to adjust for known variances but in a big ecosystem with so many evolving sources, systems, entities and agencies, it quickly gets wickedly complex.