The housing supply chain requires a constant supply of lumber products. Yet, even as housing and lumber prices have grown throughout the past decade, the underlying value of timberland remains low. We use monthly and quarterly price and inventory data to estimate a nonlinear autoregressive distributed lag model of the complete timberlumber-housing supply chain. Our methodology builds on previous lumber price studies by developing a less rigid framework for identifying nonlinear asymmetric relationships between end-use, product, and factor prices, when controlling for inventories. Our results show that in the long-run a 1% increase is housing prices corresponds to a 0.28% (95%–C.I.: 0.03%;0.53%) increase in lumber prices. Negative shocks to housing have no significant effect on lumber prices. Furthermore, we find that lumber and stumpage are only marginally cointegrated with positive and negative shocks to lumber having no significant effect on stumpage prices.
